Ryo Nishide, Ayumi Yamashita, Yumi Takaki, C. Ohta, K. Oyama, T. Ohkawa
Calving interval is often used as an indicator for fertility of beef cattle, however, maternal abilities are also required because the value of breeding cows depends on how efficiently the healthy and growing calves are produced. The calf's weight has been used as an indicator of maternity ability since the past few decades. We propose a method to estimate body weight by modeling the shape of calf using 3D information extracted from the stereo images. This method enables to predict the swelling of the cattle's body by creating a 3D model, which cannot be obtained solely from a 2D image. In addition, it is possible to estimate robust weight regardless of different shooting conditions toward cattle's posture and orientation. An image suitable for estimation is selected from motion images taken by the camera installed in the barn, and 3D coordinates are calculated by the images. Then, only the body is developed with a 3D model as it has the highest correlation with the body weight. Considering that the side of cattle's body may not be exactly perpendicular to the camera's shooting direction, a symmetric axis is extracted to find the inclination of cattle body from the camera in order to generate a 3D model based on the symmetric axis. 3D contiguous cylindrical model is used for the body of a cattle which has a rounded shape. In order to manipulate the shapes of the cylindrical surface, the circle and ellipse fittings are applied and compared. The linear regression equation of the volume of the cylindrical model and the actually measured body weight are used to estimate the cattle weight. As a result of modeling with the proposed method using the actual camera images, the correlation coefficient between the body weight and the model volume was at the best value, 0.9107. Even when experimentally examined with the different 3D coordinates obtained from other types of camera, the MAPE (Mean Absolute Percentage Error) was as low as 6.39%.
{"title":"Calf Robust Weight Estimation Using 3D Contiguous Cylindrical Model and Directional Orientation from Stereo Images","authors":"Ryo Nishide, Ayumi Yamashita, Yumi Takaki, C. Ohta, K. Oyama, T. Ohkawa","doi":"10.1145/3287921.3287923","DOIUrl":"https://doi.org/10.1145/3287921.3287923","url":null,"abstract":"Calving interval is often used as an indicator for fertility of beef cattle, however, maternal abilities are also required because the value of breeding cows depends on how efficiently the healthy and growing calves are produced. The calf's weight has been used as an indicator of maternity ability since the past few decades. We propose a method to estimate body weight by modeling the shape of calf using 3D information extracted from the stereo images. This method enables to predict the swelling of the cattle's body by creating a 3D model, which cannot be obtained solely from a 2D image. In addition, it is possible to estimate robust weight regardless of different shooting conditions toward cattle's posture and orientation. An image suitable for estimation is selected from motion images taken by the camera installed in the barn, and 3D coordinates are calculated by the images. Then, only the body is developed with a 3D model as it has the highest correlation with the body weight. Considering that the side of cattle's body may not be exactly perpendicular to the camera's shooting direction, a symmetric axis is extracted to find the inclination of cattle body from the camera in order to generate a 3D model based on the symmetric axis. 3D contiguous cylindrical model is used for the body of a cattle which has a rounded shape. In order to manipulate the shapes of the cylindrical surface, the circle and ellipse fittings are applied and compared. The linear regression equation of the volume of the cylindrical model and the actually measured body weight are used to estimate the cattle weight. As a result of modeling with the proposed method using the actual camera images, the correlation coefficient between the body weight and the model volume was at the best value, 0.9107. Even when experimentally examined with the different 3D coordinates obtained from other types of camera, the MAPE (Mean Absolute Percentage Error) was as low as 6.39%.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128816245","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}
Crowd density estimation is an important task in the surveillance camera system, it serves in security, traffic, business etc. At the present, the trend of monitoring is moving from individual to crowd, but traditional counting techniques will be inefficient in this case because of issues such as scale, clutter background and occlusion. Most of the previous methods have focused on modeling work to accurately estimate the density map and thus infer the count. However, with non-human scenes, which have many clouds, trees, houses, seas etc, these models are often confused, resulting in inaccurate count estimates. To overcome this problem, we propose the "Weighted Sum of Regressors and Human Classifier" (WSRHC) method. Our model consists of two main parts: human -- non-human classification and crowd counting estimation. First of all, we built a Human Classifier, which filters out negative sample images (non-human images) before entering into the regressors. Then, the count estimation is based on the regressors. The difference between regressors is the size of the filters. The essence of this method is the count depends on the weighted average of the density map obtained from these regressors. This is to overcome the defects of the previous model, Switching Convolutional Neural Network (Switch-CNN) select the count as the output of one of the regressors. Multi-Column Convolutional Neural Network (MCNN) combines the count and the weight of the Regressors by fixed weights from MCNN, while our approach is adapted for individual images. Our experiments have shown that our method outperform Switch-CNN, MCNN on ShanghaiTech dataset and UCF_CC_50 dataset.
{"title":"A New Framework For Crowded Scene Counting Based On Weighted Sum Of Regressors and Human Classifier","authors":"P. Do, N. Ly","doi":"10.1145/3287921.3287980","DOIUrl":"https://doi.org/10.1145/3287921.3287980","url":null,"abstract":"Crowd density estimation is an important task in the surveillance camera system, it serves in security, traffic, business etc. At the present, the trend of monitoring is moving from individual to crowd, but traditional counting techniques will be inefficient in this case because of issues such as scale, clutter background and occlusion. Most of the previous methods have focused on modeling work to accurately estimate the density map and thus infer the count. However, with non-human scenes, which have many clouds, trees, houses, seas etc, these models are often confused, resulting in inaccurate count estimates. To overcome this problem, we propose the \"Weighted Sum of Regressors and Human Classifier\" (WSRHC) method. Our model consists of two main parts: human -- non-human classification and crowd counting estimation. First of all, we built a Human Classifier, which filters out negative sample images (non-human images) before entering into the regressors. Then, the count estimation is based on the regressors. The difference between regressors is the size of the filters. The essence of this method is the count depends on the weighted average of the density map obtained from these regressors. This is to overcome the defects of the previous model, Switching Convolutional Neural Network (Switch-CNN) select the count as the output of one of the regressors. Multi-Column Convolutional Neural Network (MCNN) combines the count and the weight of the Regressors by fixed weights from MCNN, while our approach is adapted for individual images. Our experiments have shown that our method outperform Switch-CNN, MCNN on ShanghaiTech dataset and UCF_CC_50 dataset.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"50 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126351341","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}
By using a decentralized peer-to-peer network together with public and distributed ledger to decentralize the central authority, Blockchain has shown its great potential with the success of Bitcoin. However, the blockchain technology can go beyond financial transactions. In this paper, we propose an approach that utilizes the blockchain technology to issue immutable digital certificates and improve the current limitations of the existing certificate verifying systems such as faster, more trusted, and independent of the central authority. Our prototype has been successfully deployed for several short-term courses at the Center of Computer Engineering, HCMC University of Technology, Vietnam. This result indicates that our proposed system is an appropriate solution adopting ICT for e-government, especially in certificate and diploma management.
{"title":"CVSS: A Blockchainized Certificate Verifying Support System","authors":"Duc-Hiep Nguyen, Dinh-Nghia Nguyen-Duc, Nguyen Huynh-Tuong, Hoang-Anh Pham","doi":"10.1145/3287921.3287968","DOIUrl":"https://doi.org/10.1145/3287921.3287968","url":null,"abstract":"By using a decentralized peer-to-peer network together with public and distributed ledger to decentralize the central authority, Blockchain has shown its great potential with the success of Bitcoin. However, the blockchain technology can go beyond financial transactions. In this paper, we propose an approach that utilizes the blockchain technology to issue immutable digital certificates and improve the current limitations of the existing certificate verifying systems such as faster, more trusted, and independent of the central authority. Our prototype has been successfully deployed for several short-term courses at the Center of Computer Engineering, HCMC University of Technology, Vietnam. This result indicates that our proposed system is an appropriate solution adopting ICT for e-government, especially in certificate and diploma management.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131039234","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}
Nowadays, understanding sentiments of what customers say, think and review plays an important part in the success of every business. In consequence, Sentiment Analysis (SA) has been becoming a vital part in both academic and commercial standpoint in recent years. However, most of the current sentiment analysis approaches only focus on detecting the overall polarity of the whole sentence or paragraph. That is the reason why this work focuses on another approach to this task, which is Aspect Based Sentiment Analysis (ABSA). The proposed ABSA system in this paper has two main phases: aspect term extraction and aspect sentiment prediction. For the first phase, as to deal with the named-entity recognition (NER) task, it is performed by reusing the NeuroNER [1] program without any modifications because it is currently one of the best NER tool available. For the sentiment prediction task, a bidirectional gated recurrent unit (BiGRU) Recurrent Neural Network (RNN) model which processes 4 features as input: word embeddings, SenticNet [2], Part of Speech and Distance is implemented. However, this network architecture performance on SemEval 2016 [3] dataset showed some drawbacks and limitations that influenced the polarity prediction result. For this reason, this work proposes some adjustments to the mentioned model to solve the current problems and improve the accuracy of the second task.
{"title":"Aspect Based Sentiment Analysis Using NeuroNER and Bidirectional Recurrent Neural Network","authors":"N. Tran","doi":"10.1145/3287921.3287922","DOIUrl":"https://doi.org/10.1145/3287921.3287922","url":null,"abstract":"Nowadays, understanding sentiments of what customers say, think and review plays an important part in the success of every business. In consequence, Sentiment Analysis (SA) has been becoming a vital part in both academic and commercial standpoint in recent years. However, most of the current sentiment analysis approaches only focus on detecting the overall polarity of the whole sentence or paragraph. That is the reason why this work focuses on another approach to this task, which is Aspect Based Sentiment Analysis (ABSA). The proposed ABSA system in this paper has two main phases: aspect term extraction and aspect sentiment prediction. For the first phase, as to deal with the named-entity recognition (NER) task, it is performed by reusing the NeuroNER [1] program without any modifications because it is currently one of the best NER tool available. For the sentiment prediction task, a bidirectional gated recurrent unit (BiGRU) Recurrent Neural Network (RNN) model which processes 4 features as input: word embeddings, SenticNet [2], Part of Speech and Distance is implemented. However, this network architecture performance on SemEval 2016 [3] dataset showed some drawbacks and limitations that influenced the polarity prediction result. For this reason, this work proposes some adjustments to the mentioned model to solve the current problems and improve the accuracy of the second task.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"os-44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127782520","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}
Abstractive Summarization is the specific task in text generation whose popular approaches are based on the strength of Recurrent Neural Network. With the purpose to take advantages of Convolution Neural Network in text representation, we propose to combine these above networks in our encoder to capture both the global and local features from the input documents. Simultaneously, our model also integrates the reinforced mechanism with the novel reward function to get the closer direction between the learning and evaluating process. Through the experiments in CNN/Daily Mail, our models gains the significant results. Especially, in ROUGE-1 and ROUGE-L, it outperforms the previous works in this task with the expressive improvement (39.09% in ROUGE-L F1-score).
{"title":"Combined Objective Function in Deep Learning Model for Abstractive Summarization","authors":"Tung Le, Le-Minh Nguyen","doi":"10.1145/3287921.3287952","DOIUrl":"https://doi.org/10.1145/3287921.3287952","url":null,"abstract":"Abstractive Summarization is the specific task in text generation whose popular approaches are based on the strength of Recurrent Neural Network. With the purpose to take advantages of Convolution Neural Network in text representation, we propose to combine these above networks in our encoder to capture both the global and local features from the input documents. Simultaneously, our model also integrates the reinforced mechanism with the novel reward function to get the closer direction between the learning and evaluating process. Through the experiments in CNN/Daily Mail, our models gains the significant results. Especially, in ROUGE-1 and ROUGE-L, it outperforms the previous works in this task with the expressive improvement (39.09% in ROUGE-L F1-score).","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239965","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}
Le Tien Thanh, Rin Nishikawa, Masashi Takemoto, Huynh Thi Thanh Binh, H. Nakajo
Estrus is a special periods in the life cycle of female cows. Within this period, they have much more chance to become pregnant. Successfully detecting this period increase the milk and meat productivity of the whole farm. Recently, a potential approach is unsupervised learning on motion data of the cows, similar to human activity recognition based on motion. In particular, an accelerometer is attached to the neck of the cows to measure their acceleration, then the unsupervised algorithm group the measured acceleration time-series. Recent study adopted bag-of-feature and Discrete Fourier Transform for feature extraction, yet it may not reflect the nature of motion data. Thus, we proposed a method based on Discrete Wavelet Transform to get the multi-resolution feature, Dynamic Time Wraping as clustering distance and Iterative-K-Means as clustering algorithm, to better match with the characteristic of cowsâĂŹ movement. The proposed methods demonstrated higher score on human activity recognition dataset with ground truth and more reliable prediction on cow motion dataset.
{"title":"Cow estrus detection via Discrete Wavelet Transformation and Unsupervised Clustering","authors":"Le Tien Thanh, Rin Nishikawa, Masashi Takemoto, Huynh Thi Thanh Binh, H. Nakajo","doi":"10.1145/3287921.3287973","DOIUrl":"https://doi.org/10.1145/3287921.3287973","url":null,"abstract":"Estrus is a special periods in the life cycle of female cows. Within this period, they have much more chance to become pregnant. Successfully detecting this period increase the milk and meat productivity of the whole farm. Recently, a potential approach is unsupervised learning on motion data of the cows, similar to human activity recognition based on motion. In particular, an accelerometer is attached to the neck of the cows to measure their acceleration, then the unsupervised algorithm group the measured acceleration time-series. Recent study adopted bag-of-feature and Discrete Fourier Transform for feature extraction, yet it may not reflect the nature of motion data. Thus, we proposed a method based on Discrete Wavelet Transform to get the multi-resolution feature, Dynamic Time Wraping as clustering distance and Iterative-K-Means as clustering algorithm, to better match with the characteristic of cowsâĂŹ movement. The proposed methods demonstrated higher score on human activity recognition dataset with ground truth and more reliable prediction on cow motion dataset.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126760993","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}
Action recognition is one of the most important areas in the computer vision community. Many previous work use two-stream CNN model to obtain both spatial and temporal clues for predicting task. However, two stream are trained separately and combined later by late fusion. This strategy has overlooked the spatial-temporal features interaction. In this paper, we propose new two-stream CNN architectures that are able to learn the relation between two kinds of features. Furthermore, they can be trained end-to-end with standard back propagation algorithm. We also introduce a Fisher loss that makes features more discriminative. The experiments show that Fisher loss yields higher accuracy than using only the softmax loss.
{"title":"Two-stream Deep Residual Learning with Fisher Criterion for Human Action Recognition","authors":"D. V. Sang, Hoang Trung Dung","doi":"10.1145/3287921.3287972","DOIUrl":"https://doi.org/10.1145/3287921.3287972","url":null,"abstract":"Action recognition is one of the most important areas in the computer vision community. Many previous work use two-stream CNN model to obtain both spatial and temporal clues for predicting task. However, two stream are trained separately and combined later by late fusion. This strategy has overlooked the spatial-temporal features interaction. In this paper, we propose new two-stream CNN architectures that are able to learn the relation between two kinds of features. Furthermore, they can be trained end-to-end with standard back propagation algorithm. We also introduce a Fisher loss that makes features more discriminative. The experiments show that Fisher loss yields higher accuracy than using only the softmax loss.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126766670","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}
Pavement crack detection is an important problem in road maintenance. There are many processing methods, including traditional and modern methods, solving this issue. Traditional methods use edge detection or some other digital image processing for crack detection, but these approaches are sensitive to many types of noise and unwanted objects on the road. For the purpose of increasing accuracy, image pre-processing methods are required for many of these techniques. Recently, some techniques that utilize deep learning to detect cracks in images have achieved high accuracy, without pre-processing. However, some of them are very complicated, some make use of manually collected data and some methods still need some form of pre-processing. In this paper, we propose a method that applies a convolutional neural networks to detect cracks in pavement images. Our research uses two data sets, one public data set and the other collected by ourselves. We also experimentally compare our method with some exiting methods and the experiments show that the proposed approach achieves high accuracy and generates stable models.
{"title":"Pavement Crack Detection using Convolutional Neural Network","authors":"N. H. T. Nguyen, T. Lê, S. Perry, Thuy Thi Nguyen","doi":"10.1145/3287921.3287949","DOIUrl":"https://doi.org/10.1145/3287921.3287949","url":null,"abstract":"Pavement crack detection is an important problem in road maintenance. There are many processing methods, including traditional and modern methods, solving this issue. Traditional methods use edge detection or some other digital image processing for crack detection, but these approaches are sensitive to many types of noise and unwanted objects on the road. For the purpose of increasing accuracy, image pre-processing methods are required for many of these techniques. Recently, some techniques that utilize deep learning to detect cracks in images have achieved high accuracy, without pre-processing. However, some of them are very complicated, some make use of manually collected data and some methods still need some form of pre-processing. In this paper, we propose a method that applies a convolutional neural networks to detect cracks in pavement images. Our research uses two data sets, one public data set and the other collected by ourselves. We also experimentally compare our method with some exiting methods and the experiments show that the proposed approach achieves high accuracy and generates stable models.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116387962","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 task of automatic program repair is to automatically localize and generate the correct patches for the bugs. A prominent approach is to produce a space of candidate patches, then find and validate candidates on test case sets. However, searching for the correct candidates is really challenging, since the search space is dominated by incorrect patches and its size is huge. This paper presents several methods to improve the automated program repair system Prophet, called Prophet+. Our approach contributes three improvements over Prophet: 1) extract twelve relations of statements and blocks for Bi-gram model using Big code, 2) prune the search space, 3) develop an algorithm to re-rank candidate patches in the search space. The experimental results show that our proposed system enhances the performance of Prophet, recognized as the state-of-the-art system, significantly. Specifically, for the top 1, our system generates the correct patches for 17 over 69 bugs while the number achieved by Prophet is 15.
{"title":"Automated Large Program Repair based on Big Code","authors":"H. V. Thuy, Phan Viet Anh, N. X. Hoai","doi":"10.1145/3287921.3287958","DOIUrl":"https://doi.org/10.1145/3287921.3287958","url":null,"abstract":"The task of automatic program repair is to automatically localize and generate the correct patches for the bugs. A prominent approach is to produce a space of candidate patches, then find and validate candidates on test case sets. However, searching for the correct candidates is really challenging, since the search space is dominated by incorrect patches and its size is huge. This paper presents several methods to improve the automated program repair system Prophet, called Prophet+. Our approach contributes three improvements over Prophet: 1) extract twelve relations of statements and blocks for Bi-gram model using Big code, 2) prune the search space, 3) develop an algorithm to re-rank candidate patches in the search space. The experimental results show that our proposed system enhances the performance of Prophet, recognized as the state-of-the-art system, significantly. Specifically, for the top 1, our system generates the correct patches for 17 over 69 bugs while the number achieved by Prophet is 15.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132707207","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}
Jakarta has become a megacity with elaborate service network activities. Fast food restaurants as a type of food service provider have a role in supporting urban lifestyles. Despite the growth of value and transaction volume, there are some fast food categories in Indonesia which have a negative percentage of outlets growth. In general, the location of fast food restaurants divides into two categories. The first one is stand-alone restaurants, and the second is restaurants which located in other public facilities, such as malls, supermarket, and market area. According to the first law of Tobler, closer public facilities will have activity relatedness. This study aims to examine whether proximity between fast food restaurant locations and other public facilities affect categories of fast food restaurants, using spatial decision tree analysis approach. The public facilities examined for proximity to fast food restaurants consist of 11 criteria, which are considered to have a co-location pattern from previous research results. The results will be spatial characteristics of public facilities which expected to be indicators of consumer movement behavior, especially from and to fast food restaurant.
{"title":"Spatial Decision Tree Analysis to Identify Location Pattern","authors":"D. L. Widaningrum, I. Surjandari, D. Sudiana","doi":"10.1145/3287921.3287956","DOIUrl":"https://doi.org/10.1145/3287921.3287956","url":null,"abstract":"Jakarta has become a megacity with elaborate service network activities. Fast food restaurants as a type of food service provider have a role in supporting urban lifestyles. Despite the growth of value and transaction volume, there are some fast food categories in Indonesia which have a negative percentage of outlets growth. In general, the location of fast food restaurants divides into two categories. The first one is stand-alone restaurants, and the second is restaurants which located in other public facilities, such as malls, supermarket, and market area. According to the first law of Tobler, closer public facilities will have activity relatedness. This study aims to examine whether proximity between fast food restaurant locations and other public facilities affect categories of fast food restaurants, using spatial decision tree analysis approach. The public facilities examined for proximity to fast food restaurants consist of 11 criteria, which are considered to have a co-location pattern from previous research results. The results will be spatial characteristics of public facilities which expected to be indicators of consumer movement behavior, especially from and to fast food restaurant.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130848356","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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