2022 RIVF International Conference on Computing and Communication Technologies (RIVF)最新文献

Deep Reinforcement Learning Approach Using Customized Technical Indicators for A Pre-emerging Market: A Case Study of Vietnamese Stock Market 基于定制技术指标的新兴市场深度强化学习方法:以越南股市为例

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013836

Hoang Thi Hue-Thai Nguyen, Bao-Ngoc Nguyen Mac, Anh-Duy Tran, Ngoc-Thao Nguyen, D. Pham

{"title":"Deep Reinforcement Learning Approach Using Customized Technical Indicators for A Pre-emerging Market: A Case Study of Vietnamese Stock Market","authors":"Hoang Thi Hue-Thai Nguyen, Bao-Ngoc Nguyen Mac, Anh-Duy Tran, Ngoc-Thao Nguyen, D. Pham","doi":"10.1109/RIVF55975.2022.10013836","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013836","url":null,"abstract":"The Vietnamese stock market is a challenge for applying algorithmic trading. However, the advance of Machine Learning, especially Reinforcement Learning, has provided a new opportunity to develop better trading models. In this work, we proposed an ensemble strategy, namely S-A-P, combining three deep reinforcement learning models (i.e., SAC, PPO, and A2C). The best model at each quarter is used for trading in the incoming quarter based on the Sharpe ratio. Moreover, the list of technical indicators is also proposed to represent the variation in this market. Our approach shows better performance than the baseline and VN30INDEX in both profits (55% in cumulative return) and risk management (0.77 in Sharpe ratio). Additionally, this approach can perform appropriately during two high-turbulence periods, which the baseline cannot detect. The extension of this work may consider a novel Machine Learning approach for representing the stock market and a different metric for building ensemble strategy.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127126781","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

Hybrid ensemble learning approaches for cancer classification from gene expression data 基于基因表达数据的癌症分类的混合集成学习方法

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013845

Cao Truong Tran

引用次数: 0

Keynote Talk #1 RF Energy Harvesting Technology and IoT Applications 主题演讲#1射频能量收集技术和物联网应用

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/rivf55975.2022.10013834

K. Ishibashi

引用次数: 0

MSMA-Net: A Multi-scale Multidirectional Adaptation Network for Polyp Segmentation MSMA-Net:一种多尺度多向自适应息肉分割网络

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013878

Trung-Kien Le, Hoang-Minh-Quang Le, Thi-Thao Tran, Van-Truong Pham

{"title":"MSMA-Net: A Multi-scale Multidirectional Adaptation Network for Polyp Segmentation","authors":"Trung-Kien Le, Hoang-Minh-Quang Le, Thi-Thao Tran, Van-Truong Pham","doi":"10.1109/RIVF55975.2022.10013878","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013878","url":null,"abstract":"Polyps are mostly noncancerous tumors that occur in several locations in the digestive tract but are most com-monly found in the colon. But over time, some colon polyps can develop into cancer, especially Adenomas that need to be detected to remove as soon as possible. In recent years, with the variety of modern techniques for polyps detection, image segmentation using deep learning has always been an appreciated method. However, polyp segmentation in this way also has some trouble such as long dependencies, complexity computation, poor local and global context, and lack of multi-scale context. There have been many researches and techniques proposed to overcome these problems, such as attention mechanisms, atrous spatial pyramid pooling (ASPP), Receptive Field Block(RFB), etc. Inspired by those advances in deep learning, in this work, we inherited and proposed an Efficient Attention Receptive Field Block (EA-RFB Block) and Local Global Fusion (LGF) that ensures the network's multi-scale representation, capturing enough information of both local and global context. Our proposed network, namely MSMA-Net has demonstrated improved performance through two metrics of Intersection over Union (IoU) and Dice Coefficient when compared with other state-of-the-art models in Polyp datasets.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125604558","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

Solving Multiple Variable Problems by Regression Models 用回归模型求解多变量问题

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013856

Chien D. C. Ta, Linh My Ta

引用次数: 0

Effective node vaccination and containing strategies to halt SIR epidemic spreading in real-world face-to-face contact networks 有效的节点疫苗接种和遏制策略，以阻止SIR流行病在现实世界的面对面接触网络中传播

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013812

N. Nguyen, Thanh-Trung Nguyen, Tuan-Anh Nguyen, F. Sartori, M. Turchetto, F. Scotognella, R. Alfieri, D. Cassi, Q. Nguyen, M. Bellingeri

{"title":"Effective node vaccination and containing strategies to halt SIR epidemic spreading in real-world face-to-face contact networks","authors":"N. Nguyen, Thanh-Trung Nguyen, Tuan-Anh Nguyen, F. Sartori, M. Turchetto, F. Scotognella, R. Alfieri, D. Cassi, Q. Nguyen, M. Bellingeri","doi":"10.1109/RIVF55975.2022.10013812","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013812","url":null,"abstract":"We model the COVID-19 spreading by running SIR Monte-Carlo simulations in four real face-to-face contact networks. We evaluate the effectiveness of the ‘facemask use’ and ‘vaccination policies’ to curb epidemic spreading. We model the facemask use policy by assuming a lower individual infection probability $beta$. We found that while this strategy can delay the disease spreading, it does not significantly reduce the total number of infected individuals (TI), as 80% of the total population still is infected at the end of the epidemic. We model vaccination by setting individual's infection probability $beta=0$, which is equivalent to remove nodes/individuals from the network. The vaccination was found to be very effective. Even with a partial vaccination of 30% of the population nodes selected considering their centrality measure ranking, such as degree, betweenness, or PageRank, it was possible to reduce the TI of 14%. Finally, yet importantly, random partial vaccination is not effective at all, meaning that most of the unvaccinated population will be infected.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114642430","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: 0

Ensemble of Deep Neural Networks for Rice Leaf Disease Classification 基于深度神经网络的水稻叶病分类集成

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013858

Son Van Ho, H. Vuong, Binh Quang Nguyen, Quoc-Huy Trinh, Minh-Triet Tran

引用次数: 0

DA-GAN: Domain Adaptation for Generative Adversarial Networks-assisted Cyber Threat Detection DA-GAN:生成对抗网络领域自适应辅助网络威胁检测

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013804

Hien Do Hoang, Do Thi Thu Hien, Thai Bui Xuan, Tri Nguyen Ngoc Minh, Phan The Duy, V. Pham

{"title":"DA-GAN: Domain Adaptation for Generative Adversarial Networks-assisted Cyber Threat Detection","authors":"Hien Do Hoang, Do Thi Thu Hien, Thai Bui Xuan, Tri Nguyen Ngoc Minh, Phan The Duy, V. Pham","doi":"10.1109/RIVF55975.2022.10013804","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013804","url":null,"abstract":"The rising development of machine learning (ML) techniques has become the motivation for research in applying their outstanding features to facilitate intelligent intrusion detection systems (IDSs). However, ML-based solutions also have drawbacks of high false positive rates and vulnerability to sophisticated attacks such as adversarial ones. Therefore, continuous evaluation and improving those systems are necessary tasks, which can achieve by simulating mutated real-world attack scenarios. Taking advantage of the Generative Adversarial Network (GAN) and Domain Adaptation technique, our approach proposes DA-GAN, a framework that can generate mutated network attack flows. Those crafted flows then work as supplemental training data for ML-based IDS to improve its robustness in dealing with new and complicated attacks. Our framework is implemented and evaluated via experiments on the public CIC-IDS2017 and CIC-IDS2018 datasets. The results prove the effectiveness of the proposed framework in continuously strengthening ML-based IDS in the fight against network attack actors.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124009205","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 Blockchain-based approach and Attribute-based Encryption for Healthcare Record Data Exchange 基于区块链的医疗记录数据交换方法和基于属性的加密

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013886

Hien Do Hoang, Phan The Duy, Nguyen Thanh Tien, Do Thi Thu Hien, V. Pham

{"title":"A Blockchain-based approach and Attribute-based Encryption for Healthcare Record Data Exchange","authors":"Hien Do Hoang, Phan The Duy, Nguyen Thanh Tien, Do Thi Thu Hien, V. Pham","doi":"10.1109/RIVF55975.2022.10013886","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013886","url":null,"abstract":"Sharing medical data can help doctors to give a more rapid and accurate diagnosis of a patient's health problems. However, electronic healthcare records (EHRs) are also considered sensitive data, whose sharing may raise issues of security and privacy. Most current healthcare systems not only manage their data in centralized databases but also lack protection methods, which makes them vulnerable and targeted for cyberattacks. A vulnerable healthcare system then can lead to the leakage of its managed data and serious consequences. This study aims to improve the security and privacy of exchanging EHRs using blockchain technology, IPFS, and attribute-based encryption (ABE). More specifically, to overcome the weakness of centralized storage, the proposed approach uses decentralized databases in its design. We implement a permissioned blockchain network with multiple nodes to ensure the availability and secure data in case of incidents. Additionally, encryption that incorporates the owner's special attributes guarantees data privacy. The proposed system, called BABEHealth, is intended to improve the robustness of healthcare management systems and deal with known security flaws in existing systems for smart healthcare. To evaluate the performance of the proposed system, we consider the latency of transaction creation, the change in data size when applying encryption, and the encryption time with various data quantities.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129420688","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 combined CNN-LSTM and LSTM-QRNN model for prediction of Idiopathic Pulmonary Fibrosis Progression using CT Scans and Clinical Data 利用CT扫描和临床数据预测特发性肺纤维化进展的CNN-LSTM和LSTM-QRNN联合模型

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013925

Hoa Bui Thi Anh, T. T. Dinh, T. Lang, Hung Le Minh

{"title":"A combined CNN-LSTM and LSTM-QRNN model for prediction of Idiopathic Pulmonary Fibrosis Progression using CT Scans and Clinical Data","authors":"Hoa Bui Thi Anh, T. T. Dinh, T. Lang, Hung Le Minh","doi":"10.1109/RIVF55975.2022.10013925","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013925","url":null,"abstract":"Idiopathic Pulmonary Fibrosis (IPF), which causes scarred tissues and lung function damage over time, is a serious progressive lung disease. In addition, this chronic disease is irreversible, with unknown cures and unknown causes, so it is difficult to treat and becomes a challenge faced by doctors and others. Furthermore, Forced Vital Capacity (FVC) can assess the progression of lung function and it can assist to detect the disease in the early stage, so doctors have more time to give appropriate treatment and patients have more opportunities to increase survival time. Thus, the hybrid model convolutional neural network - long short-term memory (CNN-LSTM) and long short-term memory - quantile regression neural network (LSTM-QRNN) have been presented in this paper to predict FVC values by using CT scan images and clinical data. The experiment results show that the model also achieved the better modified Laplace Log Likelihood score in the private leader-board in Kaggle OSIC11https://www.kaggle.com/competitions/osic-pulmonary-fibrosis-progression dataset.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127849553","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: 0