This paper aims to develop a hybrid saxophone interface with real instrument and an electronic interface that enables to control sound and a multimedia data. Telesaxophone have developed for saxophone player with a dial sensor and several button sensors and original neck with mouthpiece combined electric interface produced by physical computing with Arduino11. We describe regarding software/hardware systems with Arduino and analysis the saxophone mechanism for interface development and demonstrate its performance of multimedia data control. In this paper, we have presented to find versatile multi-instruments that to control musical expression and various multimedia works over the trend of a new paradigm in art.
{"title":"Telesaxophone: Hybrid saxophone Interface","authors":"Euyshick Hong, Jun Kim","doi":"10.1145/3127942.3127955","DOIUrl":"https://doi.org/10.1145/3127942.3127955","url":null,"abstract":"This paper aims to develop a hybrid saxophone interface with real instrument and an electronic interface that enables to control sound and a multimedia data. Telesaxophone have developed for saxophone player with a dial sensor and several button sensors and original neck with mouthpiece combined electric interface produced by physical computing with Arduino11. We describe regarding software/hardware systems with Arduino and analysis the saxophone mechanism for interface development and demonstrate its performance of multimedia data control. In this paper, we have presented to find versatile multi-instruments that to control musical expression and various multimedia works over the trend of a new paradigm in art.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133392723","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}
Ratna Mufidah, Ito Wasito, Nurul Hanifah, M. Faturrahman, F. D. Ghaisani
Pap smear image analysis is an effective and common way for early diagnosis of cervical cancer. Nucleus and cytoplasm morphology analysis are main criterion in determining whether the cells are normal or abnormal. Therefore, the accuracy of nucleus detection is crucial before further analysis of cell changes. One of the main problem in automatic nucleus detection process on pap smear image is how to accurately detect the nucleus on multi-cell image which usually contain overlapped cells. To solve the problem, authors propose a deep learning (DL) approach in particular Stacked Sparse Autoencoder (SSAE) as a feature representation process in multi-cell pap smear images. SSAE is able to capture high level feature through learning processing from low level feature (pixel). The high level feature will be a differentiator feature between nucleus and non-nucleus. In this research, authors have applied sliding window operation (SWO) on pap smear images and utilized softmax classifier (SMC) for the nucleus classification process. The main purpose in this research is to measure the performance of SSAE+SMC for the detection of nucleus on overlapped cells. The result shows that fine-tuned SSAE+SMC has significantly increased the accuracy of nucleus detection. The best accuracy achieves 0.876 on 50 x 50 window size.
{"title":"Automatic Nucleus Detection of Pap Smear Images using Stacked Sparse Autoencoder (SSAE)","authors":"Ratna Mufidah, Ito Wasito, Nurul Hanifah, M. Faturrahman, F. D. Ghaisani","doi":"10.1145/3127942.3127946","DOIUrl":"https://doi.org/10.1145/3127942.3127946","url":null,"abstract":"Pap smear image analysis is an effective and common way for early diagnosis of cervical cancer. Nucleus and cytoplasm morphology analysis are main criterion in determining whether the cells are normal or abnormal. Therefore, the accuracy of nucleus detection is crucial before further analysis of cell changes. One of the main problem in automatic nucleus detection process on pap smear image is how to accurately detect the nucleus on multi-cell image which usually contain overlapped cells. To solve the problem, authors propose a deep learning (DL) approach in particular Stacked Sparse Autoencoder (SSAE) as a feature representation process in multi-cell pap smear images. SSAE is able to capture high level feature through learning processing from low level feature (pixel). The high level feature will be a differentiator feature between nucleus and non-nucleus. In this research, authors have applied sliding window operation (SWO) on pap smear images and utilized softmax classifier (SMC) for the nucleus classification process. The main purpose in this research is to measure the performance of SSAE+SMC for the detection of nucleus on overlapped cells. The result shows that fine-tuned SSAE+SMC has significantly increased the accuracy of nucleus detection. The best accuracy achieves 0.876 on 50 x 50 window size.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121834408","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}
Miso Ju, Jihyun Seo, Yongwha Chung, Daihee Park, Hakjae Kim
Caring individual pigs in large-scale pig farms is an important issue for preventing infectious diseases. Accordingly, many researchers have been researched about group-housed pig monitoring systems. However, it is challenging to identify individual pigs because the systems misidentify touching-pigs as a single pig. In this paper, we solve the touching-pig problem by using concave points of continuous video frames. We interpret a two dimensional outline data as a one dimensional time-series data of touching pigs and align the time-series data of continuous video frames. The experimental results show that the proposed method can segment the touching-pigs more accurate than generally used methods in real time.
{"title":"Touching-Pigs Segmentation using Concave Points in Continuous Video Frames","authors":"Miso Ju, Jihyun Seo, Yongwha Chung, Daihee Park, Hakjae Kim","doi":"10.1145/3127942.3127948","DOIUrl":"https://doi.org/10.1145/3127942.3127948","url":null,"abstract":"Caring individual pigs in large-scale pig farms is an important issue for preventing infectious diseases. Accordingly, many researchers have been researched about group-housed pig monitoring systems. However, it is challenging to identify individual pigs because the systems misidentify touching-pigs as a single pig. In this paper, we solve the touching-pig problem by using concave points of continuous video frames. We interpret a two dimensional outline data as a one dimensional time-series data of touching pigs and align the time-series data of continuous video frames. The experimental results show that the proposed method can segment the touching-pigs more accurate than generally used methods in real time.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121255388","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}
Charlie S. Marzan, Maria Jeseca C. Baculo, R. D. Bulos, Conrado R. Ruiz
Crime analysis using data mining techniques have been a possible solution to aid law enforcement officers to mitigate crime related problems. In this paper, a geospatial data analysis was conducted for detecting the hotspots of criminal activities in Manila City, Philippines. The crime records of 2012-2016 which were manually collected were geocoded and the map was generated using ArcGIS version 10. Association rules mining using Apriori algorithm was also performed on discovering frequent patterns to help the police officers to form a preventive action. This analyzed the different crimes and predicted the chance of each crime that can recur. In addition, analysis of various time series forecasting methods such as Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg to predict future trends of crime was performed. This work provides a solution to help the officers to build a crime controlling strategy to prevent crimes in the future.
{"title":"Time Series Analysis and Crime Pattern Forecasting of City Crime Data","authors":"Charlie S. Marzan, Maria Jeseca C. Baculo, R. D. Bulos, Conrado R. Ruiz","doi":"10.1145/3127942.3127959","DOIUrl":"https://doi.org/10.1145/3127942.3127959","url":null,"abstract":"Crime analysis using data mining techniques have been a possible solution to aid law enforcement officers to mitigate crime related problems. In this paper, a geospatial data analysis was conducted for detecting the hotspots of criminal activities in Manila City, Philippines. The crime records of 2012-2016 which were manually collected were geocoded and the map was generated using ArcGIS version 10. Association rules mining using Apriori algorithm was also performed on discovering frequent patterns to help the police officers to form a preventive action. This analyzed the different crimes and predicted the chance of each crime that can recur. In addition, analysis of various time series forecasting methods such as Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg to predict future trends of crime was performed. This work provides a solution to help the officers to build a crime controlling strategy to prevent crimes in the future.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122315990","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 cross-stage reverse logistics planning is a key issue in the supply chain management. This paper emphasizes to propose a mathematical model for the cross-stage reverse logistics planning problem. We focus on the analysis on how to send the good products to the downstream supply chain partners for selling to the customer and the defective products back to the upstream supply chain partners for reprocessing based on the damage style. The capacity of each partner and the customer demand are considered in the planning process. Then, the genetic algorithm is employed for solving the mathematical model. Finally, the analytical result of an illustrative example is discussed to show the quality solution gained from the proposed mathematical model and solving method.
{"title":"Cross-stage Reverse Logistics Planning via a Genetic Algorithm","authors":"Z. Che, T. Chiang, K. Hsiao, C. Chen, J. Chang","doi":"10.1145/3127942.3127944","DOIUrl":"https://doi.org/10.1145/3127942.3127944","url":null,"abstract":"The cross-stage reverse logistics planning is a key issue in the supply chain management. This paper emphasizes to propose a mathematical model for the cross-stage reverse logistics planning problem. We focus on the analysis on how to send the good products to the downstream supply chain partners for selling to the customer and the defective products back to the upstream supply chain partners for reprocessing based on the damage style. The capacity of each partner and the customer demand are considered in the planning process. Then, the genetic algorithm is employed for solving the mathematical model. Finally, the analytical result of an illustrative example is discussed to show the quality solution gained from the proposed mathematical model and solving method.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129513553","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}
We propose a novel chest boundary estimation method using a RGBD camera for robust lung electric impedance tomography (EIT). For EIT imaging, sixteen electrodes are generally used, so we use the twelve electrodes as color markers and put pattern markers on the rest four electrodes. The color and pattern markers are detect to extract surface patches. The detected pattern markers are refined by affine parameters to correctly recognize the patterns. The boundary shapes are reconstructed by the registration using translation and rotation of consecutive surface patches. In the experiment results, the relative error of a round table is 0.0169, the processing fame rate is more than 12 frames/sec without GPU (graphics processing unit), and the more robust EIT reconstruction results are shown.
{"title":"Chest boundary shape estimation using a RGBD camera and pattern markers for robust lung electrical impedance tomography","authors":"Y. Na., Yunjung Lee, Jun-Geon Kim, Daeho Lee","doi":"10.1145/3127942.3127950","DOIUrl":"https://doi.org/10.1145/3127942.3127950","url":null,"abstract":"We propose a novel chest boundary estimation method using a RGBD camera for robust lung electric impedance tomography (EIT). For EIT imaging, sixteen electrodes are generally used, so we use the twelve electrodes as color markers and put pattern markers on the rest four electrodes. The color and pattern markers are detect to extract surface patches. The detected pattern markers are refined by affine parameters to correctly recognize the patterns. The boundary shapes are reconstructed by the registration using translation and rotation of consecutive surface patches. In the experiment results, the relative error of a round table is 0.0169, the processing fame rate is more than 12 frames/sec without GPU (graphics processing unit), and the more robust EIT reconstruction results are shown.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130931706","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}
Maria Susan Anggreainy, M. R. Widyanto, B. Widjaja
Identification of individual STR-based individuals is required for the investigation of Disaster Victim Identification and other applications. The DNA identification of an individual with the DNA of both biological parents, father, and mother, would result in a perfect match value, but what if the biological parents of the individual had died. In this research, we proposed a method of identifying DNA against an individual if one or both of the individual parents were absent, so it was necessary to match the individual DNA profiles with DNA profiles of existing family members. The conclusions from the results of individual DNA matching with DNA of family members were proposed using fuzzy inference system with weighted suggestion according to familial closeness.
{"title":"Weighting for DNA Profiling","authors":"Maria Susan Anggreainy, M. R. Widyanto, B. Widjaja","doi":"10.1145/3127942.3127960","DOIUrl":"https://doi.org/10.1145/3127942.3127960","url":null,"abstract":"Identification of individual STR-based individuals is required for the investigation of Disaster Victim Identification and other applications. The DNA identification of an individual with the DNA of both biological parents, father, and mother, would result in a perfect match value, but what if the biological parents of the individual had died. In this research, we proposed a method of identifying DNA against an individual if one or both of the individual parents were absent, so it was necessary to match the individual DNA profiles with DNA profiles of existing family members. The conclusions from the results of individual DNA matching with DNA of family members were proposed using fuzzy inference system with weighted suggestion according to familial closeness.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129263466","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}
F. D. Ghaisani, Ito Wasito, M. Faturrahman, Ratna Mufidah
Cancer is one of main non-communicable diseases. Acute Lymphoblastic Leukemia (ALL), a type of white blood cancer, is one of the most common pediatric cancers. Analysis of cancer prognosis is necessary to determine the proper treatment for each patient. However, cancer data analysis is challenging because multiple risk factors may influence the prognosis of cancer, including gene and clinical condition of patient. This study aims to develop prediction model for cancer prognosis using clinical and gene expression (microarray) data. In this research, manifold learning is applied to microarray data to reduce its dimension, then two Deep Belief Network (DBN) models for both clinical and microarray data are trained separately. Probabilities obtained from Clinical DBN model and Microarray DBN model are integrated using softmax nodes on Bayesian Network structure. Based on various experiments, the best integration model obtained is DBN+BN 32 with prediction accuracy 84.2% for 2-years survival, 70.2% for 3-years, 68.4% for 4-years, and 73.7% for 5-years. This prediction model can be used in cancer analysis and help doctor to decide proper treatment for patient.
{"title":"Deep Belief Networks and Bayesian Networks for Prognosis of Acute Lymphoblastic Leukemia","authors":"F. D. Ghaisani, Ito Wasito, M. Faturrahman, Ratna Mufidah","doi":"10.1145/3127942.3127947","DOIUrl":"https://doi.org/10.1145/3127942.3127947","url":null,"abstract":"Cancer is one of main non-communicable diseases. Acute Lymphoblastic Leukemia (ALL), a type of white blood cancer, is one of the most common pediatric cancers. Analysis of cancer prognosis is necessary to determine the proper treatment for each patient. However, cancer data analysis is challenging because multiple risk factors may influence the prognosis of cancer, including gene and clinical condition of patient. This study aims to develop prediction model for cancer prognosis using clinical and gene expression (microarray) data. In this research, manifold learning is applied to microarray data to reduce its dimension, then two Deep Belief Network (DBN) models for both clinical and microarray data are trained separately. Probabilities obtained from Clinical DBN model and Microarray DBN model are integrated using softmax nodes on Bayesian Network structure. Based on various experiments, the best integration model obtained is DBN+BN 32 with prediction accuracy 84.2% for 2-years survival, 70.2% for 3-years, 68.4% for 4-years, and 73.7% for 5-years. This prediction model can be used in cancer analysis and help doctor to decide proper treatment for patient.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"342 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125237470","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 value and impact of successful capstone project can be potentially great. It can provide students involved with learning and design experiences, high confidence in design skills and abilities, and potentially long-term relationship with the sponsors. Also, the capstone project product itself can be an extremely useful application in high demand in the real world. This paper presents the three successful capstone projects from two engineering and technology enriched US institutes of higher education. The first capstone project is Bluetooth interface software for off-the-shelf for OBD-II (On-Board Diagnostic System-II) Bluetooth dongle project. The second one is Develop Mobile Single Sign-on Solution project. Finally, the last one is VMI Interactive Map project.
{"title":"The Value and Impact of Capstone Projects: Three Case Studies","authors":"S. S. Ha","doi":"10.1145/3127942.3127952","DOIUrl":"https://doi.org/10.1145/3127942.3127952","url":null,"abstract":"The value and impact of successful capstone project can be potentially great. It can provide students involved with learning and design experiences, high confidence in design skills and abilities, and potentially long-term relationship with the sponsors. Also, the capstone project product itself can be an extremely useful application in high demand in the real world. This paper presents the three successful capstone projects from two engineering and technology enriched US institutes of higher education. The first capstone project is Bluetooth interface software for off-the-shelf for OBD-II (On-Board Diagnostic System-II) Bluetooth dongle project. The second one is Develop Mobile Single Sign-on Solution project. Finally, the last one is VMI Interactive Map project.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"28 4-5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131676706","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 maternal mortality rate (MMR) is one of the indicators that describe the welfare of people in a country. There are 3 factors that caused maternal death in Indonesia. Late in recognizing the danger signs of pregnancy is one of those factors. Pregnancy risk, actually could be detected in an early stage of pregnancy. Therefore there is a need to create such an early warning information system to detect pregnancy risk so that pregnant women and health professionals could anticipate it. Furthermore, maternal mortality rate could be decreased.
{"title":"Early Warning Information System of Pregnancy Risk as an Effort to Reduce Maternal Mortality Rate","authors":"N. Yalina, D. Santi, M. Aziz","doi":"10.1145/3127942.3127963","DOIUrl":"https://doi.org/10.1145/3127942.3127963","url":null,"abstract":"The maternal mortality rate (MMR) is one of the indicators that describe the welfare of people in a country. There are 3 factors that caused maternal death in Indonesia. Late in recognizing the danger signs of pregnancy is one of those factors. Pregnancy risk, actually could be detected in an early stage of pregnancy. Therefore there is a need to create such an early warning information system to detect pregnancy risk so that pregnant women and health professionals could anticipate it. Furthermore, maternal mortality rate could be decreased.","PeriodicalId":270425,"journal":{"name":"Proceedings of the 1st International Conference on Algorithms, Computing and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128755923","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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