Omar Alghushairy, Raed Alsini, Xiaogang Ma, T. Soule
Interest in outlier detection methods is increasing because detecting outliers is an important operation for many applications such as detecting fraud transactions in credit card, network intrusion detection and data analysis in different domains. We are now in the big data era, and an important type of big data is data stream. With the increasing necessity for analyzing high-velocity data streams, it becomes difficult to apply older outlier detection methods efficiently. Local Outlier Factor (LOF) is a well-known outlier algorithm. A major challenge of LOF is that it requires the entire dataset and the distance values to be stored in memory. Another issue with LOF is that it needs to be recalculated from the beginning if any change occurs in the dataset. This research paper proposes a novel local outlier detection algorithm for data streams, called Genetic-based Incremental Local Outlier Factor (GILOF). The algorithm works without any previous knowledge of data distribution, and it executes in limited memory. The outcomes of our experiments with various real-world datasets demonstrate that GILOF has better performance in execution time and accuracy than other state-of-the-art LOF algorithms.
{"title":"A Genetic-Based Incremental Local Outlier Factor Algorithm for Efficient Data Stream Processing","authors":"Omar Alghushairy, Raed Alsini, Xiaogang Ma, T. Soule","doi":"10.1145/3388142.3388160","DOIUrl":"https://doi.org/10.1145/3388142.3388160","url":null,"abstract":"Interest in outlier detection methods is increasing because detecting outliers is an important operation for many applications such as detecting fraud transactions in credit card, network intrusion detection and data analysis in different domains. We are now in the big data era, and an important type of big data is data stream. With the increasing necessity for analyzing high-velocity data streams, it becomes difficult to apply older outlier detection methods efficiently. Local Outlier Factor (LOF) is a well-known outlier algorithm. A major challenge of LOF is that it requires the entire dataset and the distance values to be stored in memory. Another issue with LOF is that it needs to be recalculated from the beginning if any change occurs in the dataset. This research paper proposes a novel local outlier detection algorithm for data streams, called Genetic-based Incremental Local Outlier Factor (GILOF). The algorithm works without any previous knowledge of data distribution, and it executes in limited memory. The outcomes of our experiments with various real-world datasets demonstrate that GILOF has better performance in execution time and accuracy than other state-of-the-art LOF algorithms.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127910036","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}
Chronic diseases are responsible for half of annual mortality (51%) and almost half of the burden of all diseases (41%) in Bangladesh. Developing countries like Bangladesh are in a probable state of approximate loss of $7.3 trillion due to chronic diseases by 2025. Healthcare industries in Bangladesh now generate, collect, and store large amount of data. With the emergence of big data analytics, the approach to determine the factors causing specific effects on health is increasingly based on machine learning techniques. Therefore, it is important to conduct a predictive big data analysis using machine learning techniques to understand the likelihood of chronic diseases, specifically diabetes, hypertension, and heart diseases that are caused by age, income, and years of diseases. The aim of this research is to develop a predictive analytics tool for chronic diseases using machine learning techniques. The application of machine learning in the healthcare sector can minimize the costs of treatment and can help in taking proactive actions.
{"title":"Predictive Modelling for Chronic Disease: Machine Learning Approach","authors":"Md. Rakibul Hoque, Mohammed Sajedur Rahman","doi":"10.1145/3388142.3388174","DOIUrl":"https://doi.org/10.1145/3388142.3388174","url":null,"abstract":"Chronic diseases are responsible for half of annual mortality (51%) and almost half of the burden of all diseases (41%) in Bangladesh. Developing countries like Bangladesh are in a probable state of approximate loss of $7.3 trillion due to chronic diseases by 2025. Healthcare industries in Bangladesh now generate, collect, and store large amount of data. With the emergence of big data analytics, the approach to determine the factors causing specific effects on health is increasingly based on machine learning techniques. Therefore, it is important to conduct a predictive big data analysis using machine learning techniques to understand the likelihood of chronic diseases, specifically diabetes, hypertension, and heart diseases that are caused by age, income, and years of diseases. The aim of this research is to develop a predictive analytics tool for chronic diseases using machine learning techniques. The application of machine learning in the healthcare sector can minimize the costs of treatment and can help in taking proactive actions.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127180835","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}
An overloaded computer must finish what it starts and not start what will fail or hang. A congestion management algorithm, the author developed, effectively manages traffic overload with its unique formulation of Exponential Smoothing forecasting. This set of equations resolve forecasting startup issues that have limited the model's adoption as a discrete time series predictor. These expressions also satisfy implementation requirements to perform calculations using integer math and be able to reset the forecast seamlessly. A computer program, written in C language, which exercises the methodology, is downloadable from GitHub.
{"title":"Computing System Congestion Management Using Exponential Smoothing Forecasting","authors":"Ja Brady","doi":"10.1145/3388142.3388146","DOIUrl":"https://doi.org/10.1145/3388142.3388146","url":null,"abstract":"An overloaded computer must finish what it starts and not start what will fail or hang. A congestion management algorithm, the author developed, effectively manages traffic overload with its unique formulation of Exponential Smoothing forecasting. This set of equations resolve forecasting startup issues that have limited the model's adoption as a discrete time series predictor. These expressions also satisfy implementation requirements to perform calculations using integer math and be able to reset the forecast seamlessly. A computer program, written in C language, which exercises the methodology, is downloadable from GitHub.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116163049","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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