2019 11th International Conference on Knowledge and Systems Engineering (KSE)最新文献_第4页

Wearable Devices for Monitoring Dementia Sufferers: A Review and Framework for Discussion 监测痴呆症患者的可穿戴设备:综述和讨论框架

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919373

Ashok Dhakal, A. Alsadoon, P. Prasad, Angelika Maag, A. Elchouemi, Win Maung, V. Q. Nguyen

引用次数: 2

Forecasting Trading-Time based Profit-Making Strategies in Forex Industry: Using Australian Forex Data 预测交易时间为基础的盈利策略在外汇行业:使用澳大利亚外汇数据

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919432

Mihiran Rupasinghe, M. Halgamuge, N. Q. Vinh

{"title":"Forecasting Trading-Time based Profit-Making Strategies in Forex Industry: Using Australian Forex Data","authors":"Mihiran Rupasinghe, M. Halgamuge, N. Q. Vinh","doi":"10.1109/KSE.2019.8919432","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919432","url":null,"abstract":"Due to the constant fluctuation on global currency rates, it is challenging to make predictions on trading in foreign exchange (Forex) currency market without an intensive analysis; hence, traders struggle to make a profit. This study aims to analyze the relationship between the trade open time and profit in the Forex currency market to help traders to increase the chance of winning trades and make a profit. We developed a technique to observe the most suitable time duration to trade and the profit. This technique assists traders to enhance the chance of winning trades and make a profit by identifying whether it is more likely to make a profit when they keep the trade opened for a longer time or a shorter time. A Forex dataset (N=1,000,000 trades) from a third-party broker database based in Australia has been used. The collected data were filtered according to the popularity of currency pairs. Five currency pairs (as EUR vs USD, GBP vs JPY, USD vs JPY, GBP vs USD and EUR vs JPY) were further analyzed using Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel and K-Means clustering algorithms. It showed that EUR vs USD and USD vs JPY have sensitive movements of profit with the trading time. The highest profit was observed trading time in between 5 to 15 minutes. Our analysis illustrates that shorter time traders are making more profits than the longer time traders. Hence, this study demonstrates that Forex traders make a profit when the market has a unique volatile situation. This study should be useful as a reference for researches in Forex market analyses and Forex Industry to utilize profitmaking strategies.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132146919","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: 1

Deep Feature Fusion for Breast Cancer Diagnosis on Histopathology Images 基于组织病理图像的深度特征融合诊断乳腺癌

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919462

Hung Le Minh, Manh Mai Van, T. Lang

{"title":"Deep Feature Fusion for Breast Cancer Diagnosis on Histopathology Images","authors":"Hung Le Minh, Manh Mai Van, T. Lang","doi":"10.1109/KSE.2019.8919462","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919462","url":null,"abstract":"This paper presents a deep feature fusion method based on the concept of 'residual connection' of ResNet to effectively extract distinguishable features which help to improve the classification performance of the Breast cancer prediction on histopathology images. Specifically, we fuse the features extracted from different blocks of Inception-V3 to merge the features learned. The concatenated features are considered as rich information which could capture the deep features of the images. Three experiments were also conducted to investigate the three factors that may affect the classification performance: 1) Feature extractor or Fine-tuningƒ 2) Normalization vs. Non-normalization and 3) The effectiveness of our deep feature fusion method. The dataset used in this study includes 400 microscopy images collected from the ICIAR 2018 Grand Challenge on Breast Cancer histopathology images. The images are divided into 4 classes which indicate the aggressiveness levels of breast cancer, described as Normal (N), Benign (B), In Situ Carcinoma (IS) or Invasive Carcinoma (IV) according to the predominant cancer type in each image. Experimental results show that our proposed deep feature fusion method can achieve a very high classification accuracy with 95% in distinguishing 4 types of cancer classes and 97.5% for differentiating two combined groups of cancer, which are Carcinoma (N+B) and Non-carcinoma (IS+IV).","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133929531","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: 2

Clustering Automation Test Faults 集群自动化测试故障

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919435

X. Nguyen, Phu-Khoa Nguyen, Vu Nguyen

引用次数: 3

Evaluation of Maximum Likelihood Estimation and regression methods for fusion of multiple satellite Aerosol Optical Depth data over Vietnam 越南多卫星气溶胶光学深度数据融合的最大似然估计和回归方法评价

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919417

Pham Van Ha, N. X. Truong, D. Laffly, A. Jourdan, N. T. Thanh

引用次数: 1

A multi-objective cooperative coevolutionary approach for remote sensing image classification. 遥感图像分类的多目标协同进化方法。

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919371

V. Vu, L. Bui, Trung-Thanh Nguyen

{"title":"A multi-objective cooperative coevolutionary approach for remote sensing image classification.","authors":"V. Vu, L. Bui, Trung-Thanh Nguyen","doi":"10.1109/KSE.2019.8919371","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919371","url":null,"abstract":"A current problem that classification algorithms on high-resolution satellite images are addressing is the feature selection problem (FSP). The number of object’s features on satellite images is often very large. However, not all these features contribute equally to the classification results. There are still many redundant, unrelated features. It is, therefore, necessary to select these features before performing the classification step. Besides, with the selected feature set, selecting a suitable classifier also plays a very important role. In this study, the authors solve this problem with a multi-objective co-operative co-evolutionary approach (MCCA). In the MCCA, we use two populations evolving together: one population helps to find the most important set of features (named Feature population) and the other helps to get the most appropriate classifier (named Classifier population). The performance of the MCCA is examined on three satellite image datasets. From experimental results, the proposed algorithm has shown the efficiency in improving classification accuracy as well as reducing the number of characteristics.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270985","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: 1

A corpus for aspect-based sentiment analysis in Vietnamese 基于方面的越南语情感分析语料库

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919448

Minh-Hao Nguyen, T. Nguyen, D. Thin, N. Nguyen

引用次数: 9

A Compact Trace Representation Using Deep Neural Networks for Process Mining 基于深度神经网络的过程挖掘压缩轨迹表示

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919355

Hong-Nhung Bui, Trong-Sinh Vu, Tri-Thanh Nguyen, Thi-Cham Nguyen, Quang-Thuy Ha

引用次数: 2

On the Traveling Salesman Problem with Hierarchical Objective Function 带层次目标函数的旅行商问题

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919421

T. Dam, D. T. Nguyen, Q. Bui, T. Do

引用次数: 1

Performance Analysis of NOMA Beamforming Multiple Users Relay Systems NOMA波束形成多用户中继系统性能分析

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919322

Hoang Duc Vinh, Vu Van Son, T. Hoang, C. Ta, Pham Thanh Hiep

引用次数: 1