2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE)最新文献

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Flood Hazard Analytics for Urban Spaces 城市空间的洪水灾害分析

2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2018-11-01 DOI: 10.1109/ICTKE.2018.8612356

Halford M. Bermudez, Praxedis S. Marquez

{"title":"Flood Hazard Analytics for Urban Spaces","authors":"Halford M. Bermudez, Praxedis S. Marquez","doi":"10.1109/ICTKE.2018.8612356","DOIUrl":"https://doi.org/10.1109/ICTKE.2018.8612356","url":null,"abstract":"This study focuses in urban spaces such as Manila Philippines where urban locations have experienced intense flooding which leads to loss or damage of properties, destruction of homes or suspension of classes. In this point, although flood risk cannot be totally eliminated, this research will be instrumental in flood forecasting as a key tool in flood warning which can provide adequate lead time for the public to play down flood casualties. The purpose of this research is to design an application that will provide flood hazard in the next 2 to 4 hours base from users selected locations and GPS locations, using the source data from PAGASA’s hourly forecast, the system transforms the data into a notification warning via Android Application. A Rapid Application Development Prototyping was utilized by the researcher as a model during the development of the study. It used a total population purposive sampling in the evaluation performance of the system. Furthermore, the developed system was scored with a mean of 4.94 which exhibits that the respondents strongly agree with the capabilities of the developed system. It was then concluded that the developed system was able to supply Flood Hazard Analytics for Urban Spaces to guide the local government, and the Filipino people to spot possible flood behavior in a given location.","PeriodicalId":342802,"journal":{"name":"2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127573174","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}

引用次数: 3

Leukemia Prediction from Microscopic Images of Human Blood Cell Using HOG Feature Descriptor and Logistic Regression 基于HOG特征描述符和Logistic回归的人血细胞显微图像预测白血病

2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2018-09-23 DOI: 10.1109/ICTKE.2018.8612303

H. Abedy, Faysal Ahmed, Md. Nuruddin Qaisar Bhuiyan, Maheen Islam, M. Ali, M. Shamsujjoha

{"title":"Leukemia Prediction from Microscopic Images of Human Blood Cell Using HOG Feature Descriptor and Logistic Regression","authors":"H. Abedy, Faysal Ahmed, Md. Nuruddin Qaisar Bhuiyan, Maheen Islam, M. Ali, M. Shamsujjoha","doi":"10.1109/ICTKE.2018.8612303","DOIUrl":"https://doi.org/10.1109/ICTKE.2018.8612303","url":null,"abstract":"Leukemia originates in bone marrow. It massively affects the production of appropriate blood cells. Hence, its early detection is very crucial for human living. Generally, computational approaches for Leukemia detection use microscopic blood cells images. Then, machine learning based models are trained and tested for accurate measurement. The main challenge here is to achieve an acceptable accuracy with a scalable method. However, data inconsistency, missing values and data incompleteness made the researchers’ job much more difficult. In these consequences, this paper proposes a scalable Leukemia prediction method based on a publicly available ALL_IDB dataset using the HOG feature descriptor and Logistic Regression. Initially, the proposed method used Canny edge detector and noise reduction operators to detect the exact shape of Lymphocytes. Then, Principal Component Analysis (PCA) is applied to the detected image shapes. The PCA reduces the data dimensions without losing any valuable information and thus greatly minimizes the afterward computational cost. Finally, a classifier based model is produced for unforeseen events and it is tested. The results are validated using n-fold cross-validation technique, where n is a positive integer greater than or equal to three. The maximum average accuracy of the proposed model is 96% which is much higher than the state-of-the-art schemes.","PeriodicalId":342802,"journal":{"name":"2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126648422","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}

引用次数: 13

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