Titlebook: Data Mining; 17th Australasian Co Thuc D. Le,Kok-Leong Ong,Graham Williams Conference proceedings 2019 Springer Nature Singapore Pte Ltd. 2

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Interactive Deep Metric Learning for Healthcare Cohort Discoveryexperts to identify cohorts that are more relevant to a particular pre-defined purpose. Moreover, the proposed method leverages powerful deep learning-based embedding techniques to incrementally gain effective representations for the complex structures inherit in patient journey data. We experimenta

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Gruppenprodukte (Group Products)cal optima. Secondly, PGAs offer improved execution time, as each subpopulation is processed in parallel on separate threads. Our technique advances an existing GA-based method called GenClust++, by employing a PGA along with a novel information sharing technique. We also compare our technique with

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https://doi.org/10.1007/978-3-031-42141-9oughly evaluate our approach in the task of prediction of health indices of counties in the US via a large-scale dataset collected from Twitter. We also apply our proposed SPDF to two different textual features including latent topics and linguistic styles. We conduct two case studies: across-year v

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Eiji Watanabe,Takashi Ozeki,Takeshi Kohamath indices. We validate our proposed method by large-scale experiments on Twitter data for the task of predicting health indices of the US counties. Empirical results show a significant correlation with the reported health statistics, up?to a Spearman correlation coefficient (.) value of 0.69, and t

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Lecture Notes in Computer Science specially developed to imitate real-world traffic flow dataset. In the end, we assess our multi-stream learning on a historical traffic flow dataset for Thessaloniki, Greece which is published by Hellenic Institute of Transport (HIT). We obtained better results on the short-term forecasts compared

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Yuri Nishimura,Minoru Kobayashiences in phishing attack features detected for different countries. We have collected a real world Twitter dataset over 6 months and show that we are able to detect phishing successfully using US phishing models despite only a low level of phishing occurring in smaller populations such as New Zealan

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