Pub Date : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00037
Moe Hamamoto, Takashi Murakami, Hiroshi Sugimura, M. Isshiki
There are many standards for interconnectivity technology in home networks, and many of them use multicast communications. In these standards, the method to implement IGMP, a protocol for group management of multicast in IPv4, is outside the scope for the standard. Therefore, the operation of IGMP at each terminal may be different depending on the interpretation by the developer of the standard regarding IGMP. Some home routers do not implement IGMP according to any standard, so depending on the combination of the terminal and home router, interconnection using multicast communication may not be possible in some cases. In this paper, we investigate the implementation related to IGMP of home routers that prevent interconnectivity of home network technologies. In addition, we clarify the issue and propose a method to improve interconnectivity in the implementation on the terminal side against the home router issue. Furthermore, evaluation and consideration of the proposed method are described.
{"title":"Resolving IGMP Difference Among Routers and Devices for Improving Interconnectivity in Home Networks","authors":"Moe Hamamoto, Takashi Murakami, Hiroshi Sugimura, M. Isshiki","doi":"10.1109/Ubi-Media.2019.00037","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00037","url":null,"abstract":"There are many standards for interconnectivity technology in home networks, and many of them use multicast communications. In these standards, the method to implement IGMP, a protocol for group management of multicast in IPv4, is outside the scope for the standard. Therefore, the operation of IGMP at each terminal may be different depending on the interpretation by the developer of the standard regarding IGMP. Some home routers do not implement IGMP according to any standard, so depending on the combination of the terminal and home router, interconnection using multicast communication may not be possible in some cases. In this paper, we investigate the implementation related to IGMP of home routers that prevent interconnectivity of home network technologies. In addition, we clarify the issue and propose a method to improve interconnectivity in the implementation on the terminal side against the home router issue. Furthermore, evaluation and consideration of the proposed method are described.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129518425","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00040
U. Rahardja, T. Hariguna, Wiga Maaulana Baihaqi
This study aimed to analyze sentiment opinions to find out the opinions of users on an e-commerce Web. The method used was through analyzing text reviews obtained from customers on an e-commerce website. The algorithm used was k-medoid clustering.
{"title":"Opinion Mining on E-Commerce Data Using Sentiment Analysis and K-Medoid Clustering","authors":"U. Rahardja, T. Hariguna, Wiga Maaulana Baihaqi","doi":"10.1109/Ubi-Media.2019.00040","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00040","url":null,"abstract":"This study aimed to analyze sentiment opinions to find out the opinions of users on an e-commerce Web. The method used was through analyzing text reviews obtained from customers on an e-commerce website. The algorithm used was k-medoid clustering.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130184777","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00012
Yi Ming Chen, C. H. Hsu, Kuo Chung Kuo Chung
Traditional machine learning mostly uses N-gram methods for serialization data prediction, which is not only time-consuming in the pre-processing but also computationally expensive for the model. For the current common malware detection methods, a variety of features such as API, system call, control flow, and permissions are used for machine learning analysis. However, these features depend on expert analysis and to extract multiple features is also time-consuming. This study uses Dalvik opcode as a feature, which is information rich and easy to extract. However, for the time series features of the opcode, the LSTM model and other sequence models will need effective dimension reduction approach because of the long sequence problem and variable feature length, resulting in poor training performance and long training time. Some study uses the training Embedding layer and Autoencoder to reduce the feature dimension. This method requires a layer of network training time. Another method is through feature selection. This method will result in different results as long as the data set changes or the sequence semantic is lost after feature selection. Therefore, in order to solve the above problems, this paper proposes a new preprocessing method to solve the long sequence problem that the LSTM model will encounter, so as to achieve fast training and high accuracy. This study uses a new pre-processing approach combined with an LSTM model to detect malware and achieve 95.58% accuracy on Drebin 10 family and only take 45 seconds to train a model. In addition, in the face of the small training sample problems common to deep learning, this research experiment also proved effective.
{"title":"A Novel Preprocessing Method for Solving Long Sequence Problem in Android Malware Detection","authors":"Yi Ming Chen, C. H. Hsu, Kuo Chung Kuo Chung","doi":"10.1109/Ubi-Media.2019.00012","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00012","url":null,"abstract":"Traditional machine learning mostly uses N-gram methods for serialization data prediction, which is not only time-consuming in the pre-processing but also computationally expensive for the model. For the current common malware detection methods, a variety of features such as API, system call, control flow, and permissions are used for machine learning analysis. However, these features depend on expert analysis and to extract multiple features is also time-consuming. This study uses Dalvik opcode as a feature, which is information rich and easy to extract. However, for the time series features of the opcode, the LSTM model and other sequence models will need effective dimension reduction approach because of the long sequence problem and variable feature length, resulting in poor training performance and long training time. Some study uses the training Embedding layer and Autoencoder to reduce the feature dimension. This method requires a layer of network training time. Another method is through feature selection. This method will result in different results as long as the data set changes or the sequence semantic is lost after feature selection. Therefore, in order to solve the above problems, this paper proposes a new preprocessing method to solve the long sequence problem that the LSTM model will encounter, so as to achieve fast training and high accuracy. This study uses a new pre-processing approach combined with an LSTM model to detect malware and achieve 95.58% accuracy on Drebin 10 family and only take 45 seconds to train a model. In addition, in the face of the small training sample problems common to deep learning, this research experiment also proved effective.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134433410","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00020
Natthapach Anuwattananon, S. Ruengittinun
A gesture from hands and fingers have rich meanings in communication even without a word of sound. It would be very useful if a computer can understand a hand gesture. Hence, we can use a hand gesture to communicate with a robot and perform certain activities. This study focuses on tracking the position of each fingertip and palm to make a computer knows the gesture of a hand. The proposed solution was initially implemented using a MS Kinect camera while capturing a depth image of a human hand. Then, we applied some image processing algorithms to track the positions of fingertips. Finally, the result was visualized in a real-time 3D hand model based on the movements/signs given by a human hand. The experiment results indicate that the proposed approach can literally track the positions of a fingertip.
{"title":"Generating a 3D Hand Model from Position of Fingertip Using Image Processing Technique","authors":"Natthapach Anuwattananon, S. Ruengittinun","doi":"10.1109/Ubi-Media.2019.00020","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00020","url":null,"abstract":"A gesture from hands and fingers have rich meanings in communication even without a word of sound. It would be very useful if a computer can understand a hand gesture. Hence, we can use a hand gesture to communicate with a robot and perform certain activities. This study focuses on tracking the position of each fingertip and palm to make a computer knows the gesture of a hand. The proposed solution was initially implemented using a MS Kinect camera while capturing a depth image of a human hand. Then, we applied some image processing algorithms to track the positions of fingertips. Finally, the result was visualized in a real-time 3D hand model based on the movements/signs given by a human hand. The experiment results indicate that the proposed approach can literally track the positions of a fingertip.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529810","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00051
Wen-Yo Lee, C. Shih, Ti-Hung Chen, Yung-Hui Chen
This paper shows a master-slave module, which is based on a PCB board. The isomorphic circuit board can be the master or the slave through setup the configuration bits. The design can be introduced to several industrial fields, for example, the remote-based control system and the procedure control system, etc. According to the previous work of the researches, the IoT have been introduced to design the industrial equipment; nevertheless, there still have a lot of applications do not have been served. The IoT offers the information all about a system, so the equipment can be taken care anywhere anytime. In this paper, a dental system for the teeth root examining system is developed by the isomorphic master-slave module. It reduces not only the hardware complexity, but also the system development cost.
{"title":"Scalable Master-Slave Isomorphic Module for IoT Service System","authors":"Wen-Yo Lee, C. Shih, Ti-Hung Chen, Yung-Hui Chen","doi":"10.1109/Ubi-Media.2019.00051","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00051","url":null,"abstract":"This paper shows a master-slave module, which is based on a PCB board. The isomorphic circuit board can be the master or the slave through setup the configuration bits. The design can be introduced to several industrial fields, for example, the remote-based control system and the procedure control system, etc. According to the previous work of the researches, the IoT have been introduced to design the industrial equipment; nevertheless, there still have a lot of applications do not have been served. The IoT offers the information all about a system, so the equipment can be taken care anywhere anytime. In this paper, a dental system for the teeth root examining system is developed by the isomorphic master-slave module. It reduces not only the hardware complexity, but also the system development cost.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124383613","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}
The expression of Ki-67 with IHC stain has been utilized to assess the prognosis of breast cancer, and the degree of cellular differentiation and proliferation rate. Recently, some researchers utilize the index to predict metastasis of breast carcinoma. In traditional pathological screening, manual assessment of Ki-67 proliferative index may be limited by manual evaluation from different pathologists. Especially, inconsistent biopsy staining would affect the quantitation of Ki-67 proliferation so that developing an automatic system to assess Ki-67 proliferation index poses a big challenge. The goal of this paper is to propose an automatic analysis system to evaluate the degrees of Ki-67 proliferation on IHC stained cells of breast tissue using image processing and machine intelligence techniques. The proposed system not only can assist physicians diagnose, but also provides important information of treatment and prognosis. In order to validate the evaluation performance, we compared with visual assessments by a pathologist and the ImmnuoRatio (i.e., a web-based evaluation system in Ki-67 expression) developed by Vilppu J Tuominen et al.[1] via a number of Ki-67 stained samples for patients with breast carcinoma. Experimental results also demonstrate that the proposed system can automatically, accurately and reliably assess the Ki-67 proliferation index on the breast tissue images with a precision of around 87.37%. However, the accuracy evaluating with ImmunoRatio only can reach 75.82% with the same samples. Moreover, our proposed system also provides various interaction functions including browsing, navigation, and quantitative analyses for pathologists who evaluate the expression of the Ki-67 proliferation.
{"title":"A Whole Slide Ki-67 Proliferation Analysis System for Breast Carcinoma","authors":"C. Ko, Chun-Hung Lin, Chih-Hung Chuang, Chuan-Yu Chang, Shih-Hao Chang, Ji-Han Jiang","doi":"10.1109/Ubi-Media.2019.00048","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00048","url":null,"abstract":"The expression of Ki-67 with IHC stain has been utilized to assess the prognosis of breast cancer, and the degree of cellular differentiation and proliferation rate. Recently, some researchers utilize the index to predict metastasis of breast carcinoma. In traditional pathological screening, manual assessment of Ki-67 proliferative index may be limited by manual evaluation from different pathologists. Especially, inconsistent biopsy staining would affect the quantitation of Ki-67 proliferation so that developing an automatic system to assess Ki-67 proliferation index poses a big challenge. The goal of this paper is to propose an automatic analysis system to evaluate the degrees of Ki-67 proliferation on IHC stained cells of breast tissue using image processing and machine intelligence techniques. The proposed system not only can assist physicians diagnose, but also provides important information of treatment and prognosis. In order to validate the evaluation performance, we compared with visual assessments by a pathologist and the ImmnuoRatio (i.e., a web-based evaluation system in Ki-67 expression) developed by Vilppu J Tuominen et al.[1] via a number of Ki-67 stained samples for patients with breast carcinoma. Experimental results also demonstrate that the proposed system can automatically, accurately and reliably assess the Ki-67 proliferation index on the breast tissue images with a precision of around 87.37%. However, the accuracy evaluating with ImmunoRatio only can reach 75.82% with the same samples. Moreover, our proposed system also provides various interaction functions including browsing, navigation, and quantitative analyses for pathologists who evaluate the expression of the Ki-67 proliferation.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122150273","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00010
L. Yeh, Jiun-Long Huang, Ting-Yin Yen, Jen-Wei Hu
In this paper, we propose a consortium blockchainbased system sharing malicious IP to prevent further attacks happening among other hosts. In our scheme, every security operation center (SOC) serving as a blockchain-node uploads some suspicious IPs to find the potential attackers' IPs. A smart contract is responsible for comparing the loaded IPs and the existing ones without human interference. If IPs in different lists are matched with certain degree, this system will respond by giving the whole list of malicious IP. By means of these steps, shares of IP lists are achieved and attacks are prevented in advance. Besides, when uploading and sharing, we utilize elliptic curve cryptography to ensure data confidentiality and integrity.
{"title":"A Collaborative DDoS Defense Platform Based on Blockchain Technology","authors":"L. Yeh, Jiun-Long Huang, Ting-Yin Yen, Jen-Wei Hu","doi":"10.1109/Ubi-Media.2019.00010","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00010","url":null,"abstract":"In this paper, we propose a consortium blockchainbased system sharing malicious IP to prevent further attacks happening among other hosts. In our scheme, every security operation center (SOC) serving as a blockchain-node uploads some suspicious IPs to find the potential attackers' IPs. A smart contract is responsible for comparing the loaded IPs and the existing ones without human interference. If IPs in different lists are matched with certain degree, this system will respond by giving the whole list of malicious IP. By means of these steps, shares of IP lists are achieved and attacks are prevented in advance. Besides, when uploading and sharing, we utilize elliptic curve cryptography to ensure data confidentiality and integrity.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122681706","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00013
Leo Willyanto Santoso
Nowadays, there is a growing of interest about cloud technology to many companies around the world. That's why many companies trying and implementing cloud computing technologies in their business processes. This research will examine the security requirements that will apply for companies and organizations when they choose to move to a cloud service solution. The study is carried out because cloud services are very desirable in many industries today. Migrating to cloud services would often results in great benefits both financially and administratively. The concerns raised by the transition are how security should be handled. Many companies suffer from a lack of knowledge and it is seen as a big risk to make the transition. This leads to the question that the research strive to answer - which security demands will the transition to a cloud service implicate? In this paper we explain which security requirements are available both for local solutions and cloud solutions. We draw conclusions about what differences there are, what requirements are mutual, which ones are new and which ones are absent if a transition is made to cloud services. The result of this research is an evaluation that companies and organizations can use as a basis when they plan to implement this particular transition.
{"title":"Cloud Technology: Opportunities for Cybercriminals and Security Challenges","authors":"Leo Willyanto Santoso","doi":"10.1109/Ubi-Media.2019.00013","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00013","url":null,"abstract":"Nowadays, there is a growing of interest about cloud technology to many companies around the world. That's why many companies trying and implementing cloud computing technologies in their business processes. This research will examine the security requirements that will apply for companies and organizations when they choose to move to a cloud service solution. The study is carried out because cloud services are very desirable in many industries today. Migrating to cloud services would often results in great benefits both financially and administratively. The concerns raised by the transition are how security should be handled. Many companies suffer from a lack of knowledge and it is seen as a big risk to make the transition. This leads to the question that the research strive to answer - which security demands will the transition to a cloud service implicate? In this paper we explain which security requirements are available both for local solutions and cloud solutions. We draw conclusions about what differences there are, what requirements are mutual, which ones are new and which ones are absent if a transition is made to cloud services. The result of this research is an evaluation that companies and organizations can use as a basis when they plan to implement this particular transition.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128487878","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00033
Yuan-Tsung Chang, T. Shih
Deep learning is a method that is very commonly used in image recognition. We use the SIFT method to extract feature points, so that the machine can detect the objects in the motion images and they can be integrated into the operation of the robot arm to judge and capture specific objects. This method is also used to detect whether the parameters of the object meet the predetermined values. It will provide a warning if the predetermined values are not met. This can be used to identify the good and defective products on the production line. In the CNN database we have trained more than 30,000 images and improved the last step of SIFT algorithm to demonstrate that our new method can achieve better accuracy.
{"title":"A Deep Learning Approach for Dynamic Object Understanding Using SIFT","authors":"Yuan-Tsung Chang, T. Shih","doi":"10.1109/Ubi-Media.2019.00033","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00033","url":null,"abstract":"Deep learning is a method that is very commonly used in image recognition. We use the SIFT method to extract feature points, so that the machine can detect the objects in the motion images and they can be integrated into the operation of the robot arm to judge and capture specific objects. This method is also used to detect whether the parameters of the object meet the predetermined values. It will provide a warning if the predetermined values are not met. This can be used to identify the good and defective products on the production line. In the CNN database we have trained more than 30,000 images and improved the last step of SIFT algorithm to demonstrate that our new method can achieve better accuracy.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125562793","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 : 2019-08-01DOI: 10.1109/Ubi-Media.2019.00036
Yu-Hsiang Chang, Hung-Chin Jang
The prosperity of the social economy, tourism, and entertainment industry are important factors to cause traffic congestion. In addition to commuting hours and holidays, if a large-scale event, such as a concert, a sporting event or an exhibition is held, it is easy to make traffic congestion even worse. If we know in advance the time and place of the large-scale event, then we can accurately forecast the future traffic flow and plan the driving route. It helps effectively relieve traffic flow, reduce travel time and carbon emissions. In this study, we used the Vehicle Detector (VD) [12] data from the Taipei City Government Open Data Platform as a source of regular traffic data as well as the data of Forecastable Sporadic Event (FSE), such as a large-scale event, to forecast traffic flow. The information of time and place of the FSE are collected from various information websites (ticketing websites, tourist websites, etc.) by web crawlers. We proposed a Long Short-Term Memory (LSTM) deep learning model for traffic flow forecast, which was trained with both VD and FSE data. We further used Adam Optimizer to adjust the weight and bias of the model to optimize the forecast accuracy. The implementation of the LSTM model was conducted in TensorFlow, a machine learning framework developed by Google. Finally, we evaluated the forecast accuracy of the model by Mean Absolute Percentage Error (MAPE) and analyzed the effectiveness of applying FSE data to traffic forecast.
{"title":"Traffic Flow Forecast for Traffic with Forecastable Sporadic Events","authors":"Yu-Hsiang Chang, Hung-Chin Jang","doi":"10.1109/Ubi-Media.2019.00036","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00036","url":null,"abstract":"The prosperity of the social economy, tourism, and entertainment industry are important factors to cause traffic congestion. In addition to commuting hours and holidays, if a large-scale event, such as a concert, a sporting event or an exhibition is held, it is easy to make traffic congestion even worse. If we know in advance the time and place of the large-scale event, then we can accurately forecast the future traffic flow and plan the driving route. It helps effectively relieve traffic flow, reduce travel time and carbon emissions. In this study, we used the Vehicle Detector (VD) [12] data from the Taipei City Government Open Data Platform as a source of regular traffic data as well as the data of Forecastable Sporadic Event (FSE), such as a large-scale event, to forecast traffic flow. The information of time and place of the FSE are collected from various information websites (ticketing websites, tourist websites, etc.) by web crawlers. We proposed a Long Short-Term Memory (LSTM) deep learning model for traffic flow forecast, which was trained with both VD and FSE data. We further used Adam Optimizer to adjust the weight and bias of the model to optimize the forecast accuracy. The implementation of the LSTM model was conducted in TensorFlow, a machine learning framework developed by Google. Finally, we evaluated the forecast accuracy of the model by Mean Absolute Percentage Error (MAPE) and analyzed the effectiveness of applying FSE data to traffic forecast.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134329774","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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