{"title":"A Novel Technique for Solving Fully Fuzzy Nonlinear Systems Based on Neural Networks","authors":"R. Jafari, S. Razvarz, A. Gegov","doi":"10.1142/s2196888820500050","DOIUrl":"https://doi.org/10.1142/s2196888820500050","url":null,"abstract":"Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irred...","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122275617","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}
R. Kozik, M. Choraś, M. Pawlicki, W. Holubowicz, Dirk Pallmer, W. Müller, Ernst-Josef Behmer, Ioannis V. Loumiotis, K. Demestichas, Roxana Horincar, C. Laudy, David Faure
{"title":"Common Representational Model and Ontologies for Effective Law Enforcement Solutions","authors":"R. Kozik, M. Choraś, M. Pawlicki, W. Holubowicz, Dirk Pallmer, W. Müller, Ernst-Josef Behmer, Ioannis V. Loumiotis, K. Demestichas, Roxana Horincar, C. Laudy, David Faure","doi":"10.1142/s2196888820020017","DOIUrl":"https://doi.org/10.1142/s2196888820020017","url":null,"abstract":"Ontologies have developed into a prevailing technique for establishing semantic interoperability among heterogeneous systems transacting information. An ontology is an unambiguous blueprint of a co...","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122301708","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}
{"title":"Simultaneous Removal of Prefix and Suffix","authors":"Pawan Tamta, B. P. Pande","doi":"10.1142/s2196888820500074","DOIUrl":"https://doi.org/10.1142/s2196888820500074","url":null,"abstract":"This work is an attempt to devise a Stemmer that can remove both prefix and suffix together from a given word in English language. For a given input word, our method considers all possible internal...","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116933021","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}
Ja-Hwung Su, Chu-Yu Chin, Yi-Wen Liao, Hsiao-Chuan Yang, V. Tseng, S. Hsieh
{"title":"A Personalized Music Recommender System Using User Contents, Music Contents and Preference Ratings","authors":"Ja-Hwung Su, Chu-Yu Chin, Yi-Wen Liao, Hsiao-Chuan Yang, V. Tseng, S. Hsieh","doi":"10.1142/s2196888820500049","DOIUrl":"https://doi.org/10.1142/s2196888820500049","url":null,"abstract":"Recently, the advances in communication technologies have made music retrieval easier. Without downloading the music, the users can listen to music through online music websites. This incurs a chal...","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166247","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}
{"title":"Information Systems Development with the Help of Petri Nets","authors":"J. Pokorný, Karel Richta, T. Richta","doi":"10.1142/s2196888820500025","DOIUrl":"https://doi.org/10.1142/s2196888820500025","url":null,"abstract":"Many present software systems can be developed by a sequence of transformations from the source specification to the final implementation. An interesting question is whether we can support such a sequence of transformations by some formal apparatus that enables to verify succeeding steps of development, and finally also the whole development process. As an example, we use the transformation of a definition of the set of system nodes defined as classical workflow models, and then transform them into a set of Petri nets representing the target system implementation. Such a transformation supports development of software systems, whose specification is based on classical workflow models, but the implementation is based on Petri nets. Each part of the designed system is translated from workflow model into a set of Petri nets, and interpreted by the set of special Petri Nets Virtual Machines (PNVMs) which are installed on all nodes of the system. The method is illustrated on the example of house heating system.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128923517","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}
{"title":"Process, Analyze and Visualize Telecommunication Network Configuration Data in Graph Database","authors":"P. Lehotay-Kéry, A. Kiss","doi":"10.1142/s2196888820500037","DOIUrl":"https://doi.org/10.1142/s2196888820500037","url":null,"abstract":"In network telemetry systems, nodes produce vast number of configuration files based on how they are configured. Steps were taken to process these files into databases to help the work of the devel...","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758186","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}
Nahian Ahmed, Nazmul Alam Diptu, M. Shadhin, M. A. F. Jaki, M. Hasan, M. Islam, R. Rahman
{"title":"Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches","authors":"Nahian Ahmed, Nazmul Alam Diptu, M. Shadhin, M. A. F. Jaki, M. Hasan, M. Islam, R. Rahman","doi":"10.1142/S2196888819500246","DOIUrl":"https://doi.org/10.1142/S2196888819500246","url":null,"abstract":"Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123614095","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}
{"title":"Version-Compatible HL7 Parser Based on Object-Oriented Design","authors":"Dinh Tuyen Hoang, I. K. Lee, D. Hwang","doi":"10.1142/S2196888819500258","DOIUrl":"https://doi.org/10.1142/S2196888819500258","url":null,"abstract":"International standards for the exchange of healthcare information, known as Health Level Seven (HL7), were developed for the interoperability of healthcare information systems. Because of HL7’s complex structure and syntax, HL7 messages are processed by computer software. HL7 defines that, when the version is updated, it should be compatible with the previous version. However, most of the HL7 interface software that is currently in development does not consider the version compatibility of HL7 messages. Instead, a separate conversion software module has been used to handle the version compatibility of HL7 messages for the healthcare information system. However, such a method is inefficient, because it requires several hours and incurs a huge cost. Therefore, in this study, an HL7 parser was developed that not only supports backward compatibility with older versions in accordance with the version compatibility definition of the HL7 V2 messages but also guarantees forward compatibility with newer versions to enhance its utilization. The developed parser was used to test conversion between different versions of HL7 V2 messages which were created to transmit information from one healthcare device to another. Through this test, the usefulness of the developed parser was verified.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129692186","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}
{"title":"Improving Named Entity Recognition in Vietnamese Texts by a Character-Level Deep Lifelong Learning Model","authors":"Ngoc-Vu Nguyen, Thi-Lan Nguyen, Cam-Van Nguyen Thi, Mai-Vu Tran, Tri-Thanh Nguyen, Quang-Thuy Ha","doi":"10.1142/s219688881950026x","DOIUrl":"https://doi.org/10.1142/s219688881950026x","url":null,"abstract":"Named entity recognition (NER) is a fundamental task which affects the performance of its dependent task, e.g. machine translation. Lifelong machine learning (LML) is a continuous learning process, in which the knowledge base accumulated from previous tasks will be used to improve future learning tasks having few samples. Since there are a few studies on LML based on deep neural networks for NER, especially in Vietnamese, we propose a lifelong learning model based on deep learning with a CRFs layer, named DeepLML–NER, for NER in Vietnamese texts. DeepLML–NER includes an algorithm to extract the knowledge of “prefix-features” of named entities in previous domains. Then the model uses the knowledge in the knowledge base to solve the current NER task. Preprocessing and model parameter tuning are also investigated to improve the performance. The effect of the model was demonstrated by in-domain and cross-domain experiments, achieving promising results.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116871333","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}
{"title":"Investigating Recommendation Algorithms for Escape Rooms","authors":"Sagi Bazinin, Guy Shani","doi":"10.1142/S2196888819500209","DOIUrl":"https://doi.org/10.1142/S2196888819500209","url":null,"abstract":"An escape room is a physical puzzle solving game, where participants solve a series of riddles within a limited time to exit a locked room. Escape rooms differ in their theme, environment, and difficulty, and people hence often differ on their preferences over escape rooms. As such, recommendation systems can help people in deciding which room to visit. In this paper, we describe the properties of the escape rooms recommendation problem, with respect to other popular recommendation problems. We describe a dataset of reviews collected within a current system. We provide an empirical comparison between a set of recommendation algorithms over two problems, top-N recommendation and rating prediction. In both cases, a KNN method performed the best.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123016966","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}