2020 International Conference on Decision Aid Sciences and Application (DASA)最新文献_第4页

An overview of Intrusion Detection Based on Deep Learning Techniques 基于深度学习技术的入侵检测综述

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317074

Fadheela Hussain, M. Hammad

{"title":"An overview of Intrusion Detection Based on Deep Learning Techniques","authors":"Fadheela Hussain, M. Hammad","doi":"10.1109/DASA51403.2020.9317074","DOIUrl":"https://doi.org/10.1109/DASA51403.2020.9317074","url":null,"abstract":"One of the main challenge in computer networks has become to keep up with patterns of threats that evolve and increase on a daily basis. There are many traditional mechanisms, such as firewalls, but they do not secure the detection of new types of attacks. Intrusion detection systems are tools to detect attacks, but they suffer from an inability to detect unknown attacks. Therefore, the method was devoted to methods of machine learning and data mining to increase the capability to predict new types of attacks. This study reviewed and analysed the research background for Intrusion Detection Systems (IDSs) based on Deep Learning (DL) or Machine Learning (ML) methods into a logical taxonomy and pinpoints the challenges and future opportunities in this vital study area. There are several techniques to aid IDS to identify and identified the changing behaviour of the system. However, some papers have lately proposed the idea of hybrid detection. This study analyses machine-learning techniques in IDS. Many related studies focused on machine learning techniques had been reviews in the period below 2000 to 2020. Associated studies include machine learning, deep learning, and Hybrid approaches.","PeriodicalId":106950,"journal":{"name":"2020 International Conference on Decision Aid Sciences and Application (DASA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124020871","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

Machine Learning for Malware Detection on Balanced and Imbalanced Datasets 基于平衡和不平衡数据集的机器学习恶意软件检测

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317206

Manish Goyal, Raman Kumar

引用次数: 3

Statistical and Probabilistic-based Decision aids for Offshore Wind Turbine Reliability 基于统计和概率的海上风力发电机可靠性决策辅助

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317258

Nathan R Dappa, S. Sriramula

引用次数: 1

New Combining Rules for Spatial Clustering Methods Using Sigma-Count for Spatial Epidemiology 基于西格玛计数的空间流行病学聚类新组合规则

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317161

Laísa Ribeiro de Sá, L. S. Machado, Jordana de Almeida Nogueira, R. M. de Moraes

引用次数: 0

Modeling evapotranspiration using Encoder-Decoder Model 利用编码器-解码器模型模拟蒸散发

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317100

Khadijeh Alibabaei, P. Gaspar, T. M. Lima

{"title":"Modeling evapotranspiration using Encoder-Decoder Model","authors":"Khadijeh Alibabaei, P. Gaspar, T. M. Lima","doi":"10.1109/DASA51403.2020.9317100","DOIUrl":"https://doi.org/10.1109/DASA51403.2020.9317100","url":null,"abstract":"In recent years, deep learning algorithms successfully applied to develop decision support systems in fields such as medical, bibliometric, and many more. The field of agriculture is one of the most important where developing decision support systems using deep learning needs to be explored due to its direct impact on human well-being. Agriculture is the largest customer of freshwater. Hence, optimizing irrigation and developing an efficient irrigation decision support systems is essential. The amount of evapotranspiration is the most critical factor in scheduling irrigation. In this work, the ability of an Encoder-Decoder Long Short-Term Memory model to model the daily evapotranspiration was investigated for three locations in Portugal. The model predicts evapotranspiration three days into the future, given six days of historical data. Two models were trained; one is used the teacher forcing method and the other one without the teacher forcing method. Our results show that by using the teacher forcing method, the model's performance could be improved. The performance of the Encoder-Decoder Long Short-Term Memory was compared to a simple Long Short-Term Memory model, Convolutional Neural Network, a Multi-Layer Perceptions model, and Random Forest regression. The Encoder-Decoder Long-short term memory performed better on the test set.","PeriodicalId":106950,"journal":{"name":"2020 International Conference on Decision Aid Sciences and Application (DASA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128737130","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

Research paper classification based on Word2vec and community discovery 基于Word2vec和社区发现的论文分类研究

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317101

Esra Gündogan, Mehmet Kaya

{"title":"Research paper classification based on Word2vec and community discovery","authors":"Esra Gündogan, Mehmet Kaya","doi":"10.1109/DASA51403.2020.9317101","DOIUrl":"https://doi.org/10.1109/DASA51403.2020.9317101","url":null,"abstract":"With the advances in information technologies, different research areas are emerging day by day and thousands of research papers are published in these fields. Papers are not presented to users grouped according to their topics. Therefore, it is getting harder and harder for people to access research papers in their field of interest. To facilitate this search process, a paper classification system is proposed in this study to categorize papers on similar topics. A different approach from the studies presented for classification so far has been proposed. This system is a complex system created by combining Word2vec, network modeling and community discovery. With the Word2vec method, which has attracted great attention recently, paper similarities have been found, a network based on similarity rates has been created and papers have been clustered with the community discovery algorithm. As a result of the application with the proposed system, a success of 89% has been achieved. As can be seen from the results, this approach presented for an important classification problem will provide great convenience to people. It will enable fast and efficient access to papers.","PeriodicalId":106950,"journal":{"name":"2020 International Conference on Decision Aid Sciences and Application (DASA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792387","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: 5

Strategic Intelligence and Knowledge Management as drivers of Decision-Making in Mining Industry: An analysis of the literature 战略情报与知识管理:矿业企业决策的驱动因素:文献分析

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317053

A. Faz-Mendoza, N. Gamboa-Rosales, A. Castorena-Robles, M. Cobo, R. Castañeda-Miranda, J. López-robles

{"title":"Strategic Intelligence and Knowledge Management as drivers of Decision-Making in Mining Industry: An analysis of the literature","authors":"A. Faz-Mendoza, N. Gamboa-Rosales, A. Castorena-Robles, M. Cobo, R. Castañeda-Miranda, J. López-robles","doi":"10.1109/DASA51403.2020.9317053","DOIUrl":"https://doi.org/10.1109/DASA51403.2020.9317053","url":null,"abstract":"One of the manifestations of the knowledge society is the increase in areas of uncertainty, transforming ignorance, understood as the ignorance of non-knowledge, into uncertainty, understood as the knowledge of non-knowledge. Knowledge management and strategic intelligence are transformative activities of society, implicitly connected with innovation in sustainability and business changes that result in greater organizational potential, and in a knowledge economy. The objective of Intelligence is to obtain value from data and information by analyzing a large amount of data. Mining and mineral extraction processes are a sector that due to its complexity requires tools for decision-making that ensure efficiency and competitive position without forgetting the stakeholders. Therefore, this study performs a conceptual and bibliometric analysis of production in the area of Knowledge Management and Intelligence quantifying performance indicators, identifying the main research areas and authors, and evaluating the development of the knowledge field using VOSviewer as bibliometric analysis software.","PeriodicalId":106950,"journal":{"name":"2020 International Conference on Decision Aid Sciences and Application (DASA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127326555","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

Optimistic disclosure tone in corporate annual reporting and financial performance 公司年报和财务业绩披露基调乐观

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317105

Z. AlAlwani, G. Mousa

引用次数: 0

Promoting the Benefits of ESCO Projects at University of Bahrain 促进巴林大学ESCO项目的效益

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317052

F. Janahi, M. B. Shams

引用次数: 0

Comparative analysis on carbon footprint of hydrogen fuel cell and battery electric vehicles based on the GREET model 基于GREET模型的氢燃料电池与纯电动汽车碳足迹对比分析

2020 International Conference on Decision Aid Sciences and Application (DASA) Pub Date : 2020-11-08 DOI: 10.1109/DASA51403.2020.9317020

E. Y. Wong, D. C. Ho, Stuart C. K. So, C. Tsang, Eve Man Hin Chan

{"title":"Comparative analysis on carbon footprint of hydrogen fuel cell and battery electric vehicles based on the GREET model","authors":"E. Y. Wong, D. C. Ho, Stuart C. K. So, C. Tsang, Eve Man Hin Chan","doi":"10.1109/DASA51403.2020.9317020","DOIUrl":"https://doi.org/10.1109/DASA51403.2020.9317020","url":null,"abstract":"Facing global warming and new policies on banning the use of diesel in vehicles, there is a growing need in developing vehicles using renewable energy to mitigate carbon emissions in the transport and logistics sector. Among different forms of non-fossil energy for vehicles, hydrogen-powered fuel cell emerges as a promising way to combat global warming. However, previous studies on vehicle carbon emissions focus mainly on diesel and electric vehicles (EVs). Besides, as most of emission assessment methodologies on products are developed for fast-moving consumer goods, there is insufficient research on the product carbon footprint (PCF) of hydrogen-powered vehicles. To address that gap, this study evaluates the life-cycle assessment (LCA) process of hydrogen fuel cell vehicle and compare the PCF of an EV (Tesla Model 3) and a hydrogen fuel cell car (Toyota MIRAI) based on the life cycle assessment model of GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation). This study found that that the fuel cycle of GREET1 contributes significantly to the vehicle's PCF. Besides, higher transparency in disclosure of relevant data in the PCF methodology adopted by vehicle manufacturers is needed for making comparison of their vehicles' emissions possible. Future research needs to examine the best practices of PCF calculation and reporting for new energy vehicles including private cars and trucks. Future development on simulating various types of hydrogen fuel cells for vehicles and their corresponding emissions are suggested.","PeriodicalId":106950,"journal":{"name":"2020 International Conference on Decision Aid Sciences and Application (DASA)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132966381","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