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检索条件"主题词=Learning algorithms"

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ITcg's participation at mex-a3t 2020: Aggressive identification and fake news detection based on textual features for mexican Spanish

ITcg's participation at mex-a3t 2020: Aggressive identificat...

引用

2020 Iberian Languages Evaluation Forum, IberLEF 2020

作者： Zaizar-Gutiérrez, Diego Fajardo-Delgado, Daniel Álvarez-Carmona, Miguel A. Tecnológico Nacional de México/ Campus Ciudad Guzmán Mexico Mexico Mexico

This paper explains our approach to Aggressiveness Identification and Fake News Classification in the 2020 MEX-A3T shared task. The tasks propose a binary classification for both tasks (aggressive and nonaggressive or fake news and non-fake news). We approached the problem using simple basic methods of features selection and terms weighing. We trained with a set of machine learning algorithms. Our best run for aggressiveness identification achieved an accuracy of 0.81, where the best result obtained 0.88. On the other hand, for the aggressiveness identification, our accuracy result was 0.78, where the best result was 0.85. © 2020 Copyright for this paper by its authors. Use permitted under.

关键词： learning algorithms

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Association Rule Modeling using UML and Apriori Algorithm 1

Association Rule Modeling using UML and Apriori Algorithm

引用

1st International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

作者： Angulakshmi, M. Deepa, M. Sudha, S. Brindha, K. Vellore Institute of Technology School of Information Technology and Engineering Vellore India

ISBN: (纸本)9781728141428

The categorical relationships play a crucial role for choosing of computer database. Based on it, association rules display the link among the 2 categories. Because the object oriented elegance for the computer code cultivates, the relationships among the categories. Additionally rise near the complicated computer code style. However, one will develop improved object-oriented style through right association rules among groups or categories. The present paper deals to style the proper association rules for the object-oriented databases taken from the categories. A true scenario of Electricity Bill Deposit System (EBDS) and user interested items by analyzing user behavior history in social network environment. Both applications are taken into account in Indian situation and Apriori Algorithmic rule is employed for locating the employment of recurrent information sets through the proper association rules. The association rules area unit deliberate through famous Unified Modeling Language. The current paper work is to the associate degree carrying out of Apriori algorithmic rule towards the information of EBDS. © 2020 IEEE.

关键词： learning algorithms

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Overview of the shared task on fake news detection in urdu at FIRE 2020 12

Overview of the shared task on fake news detection in urdu a...

引用

Working Notes of FIRE - 12th Forum for Information Retrieval Evaluation, FIRE-WN 2020

作者： Amjad, Maaz Sidorov, Grigori Zhila, Alisa Gelbukh, Alexander Rosso, Paolo Mexico Independent Researcher United States Universitat Politècnica de València Spain

This overview paper describes the first shared task on fake news detection in Urdu language. The task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. The dataset contained news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural networks techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning techniques. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： learning algorithms

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A Homogeneous-Heterogeneous Ensemble of Classifiers 27th

A Homogeneous-Heterogeneous Ensemble of Classifiers

引用

27th International Conference on Neural Information Processing, ICONIP 2020

作者： Luong, Anh Vu Vu, Trung Hieu Nguyen, Phuong Minh Van Pham, Nang McCall, John Liew, Alan Wee-Chung Nguyen, Tien Thanh School of Information and Communication Technology Griffith University Brisbane Australia School of Electronics and Telecommunications Hanoi University of Science and Technology Hanoi Viet Nam Department of Environmental Technology Faculty of Environmental Sciences VNU University of Science Vietnam National University Hanoi Viet Nam School of Electrical Engineering Hanoi University of Science and Technology Hanoi Viet Nam School of Computing Robert Gordon University Aberdeen United Kingdom

ISBN: (纸本)9783030638221

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module. © 2020, Springer Nature Switzerland AG.

关键词： learning algorithms

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Some statistical and CI models to predict chaotic high-frequency financial data

Some statistical and CI models to predict chaotic high-frequ...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2019

作者： Marcek, Dusan Madera, Martin Department of Applied Informatics Faculty of Economics VŠB – Technical University of Ostrava Sokolská tř. 33 Ostrava70200 Czech Republic

ISBN: (纸本)9783030237554

Forecasting of financial time series data is a complex problem, which has benefited from recent advancements and research in machine learning. To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARMA models. As a competitive tool to statistical forecasting models, we use the popular classic neural network of perceptron type. To train neural networks, the BP algorithm and heuristics like genetic and micro-genetic algorithm are implemented. A comparative analysis of selected learning methods is also performed and evaluated. © 2020, Springer Nature Switzerland AG.

关键词： learning algorithms

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NuCDS: An efficient local search algorithm for minimum connected dominating set 29

NuCDS: An efficient local search algorithm for minimum conne...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Bohan Zhang, Xindi Cai, Shaowei Lin, Jinkun Wang, Yiyuan Blum, Christian State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer Science and Information Technology Northeast Normal University China Campus of the UAB Bellaterra Spain

ISBN: (纸本)9780999241165

The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Despite its practical importance, there are few works on solving MCDS for massive graphs, mainly due to the complexity of maintaining connectivity. In this paper, we propose two novel ideas, and develop a new local search algorithm for MCDS called NuCDS. First, a hybrid dynamic connectivity maintenance method is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and its previous counterpart. Second, we define a new vertex property called safety to make the algorithm more considerate when selecting vertices. Experiments show that NuCDS significantly outperforms the state-of-the-art MCDS algorithms on both massive graphs and classic benchmarks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： learning algorithms

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Intelligent scheduling for stratosphere cloud platforms

Intelligent scheduling for stratosphere cloud platforms

引用

2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020

作者： Periola, A.A. Alonge, A.A. Ogudo, K.A. Department of Electrical and Electronics Engineering Technology University of Johannesburg South Africa

ISBN: (数字)9781728167701

ISBN: (纸本)9781728167701

Data centres play an important role in developing machine learning algorithms and in wireless networks. The large scale use of data centres increases the operational costs for cloud computing service providers. The operational costs can be reduced by siting data centres in locations such as the stratosphere. The siting of data centres in stratosphere reduces cooling costs and enhances the power usage effectiveness (PUE). This paper examines the PUE improvement obtainable with data centres located in the stratosphere. Performance evaluation shows that the stratosphere’s cooling capacity increases with the proportion of green house gases. The PUE is enhanced by at least 27% on average when 33.3% of data centres in a cloud platform are located in the stratosphere. The PUE is enhanced by up to 40.1% on average when 50% of data centres in a cloud platform are located in the stratosphere. ©2020 IEEE.

关键词： learning algorithms

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The intellectual system development of distant competencies analyzing for IT recruitment 14th

The intellectual system development of distant competencies ...

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14th International Scientific and Technical Conference on Computer Science and Information Technologies, CSIT 2019

作者： Rzheuskyi, Antonii Kutyuk, Orest Voloshyn, Orysia Kowalska-Styczen, Agnieszka Voloshyn, Viktor Chyrun, Lyubomyr Chyrun, Sofiia Peleshko, Dmytro Rak, Taras Lviv Polytechnic National University Lviv Ukraine Silesian University of Technology Gliwice Poland IT STEP University Lviv Ukraine

ISBN: (纸本)9783030336943

AHP study methods of solving problems to use AHP comparing alternatives (TOPSIS) and supervised learning algorithm using linear regression are proposed. To meet the challenges these methods were chosen language PHP, its own established framework CollEntRes, additional tools that helped during the programming process to detect errors and correct them (Debug), popular modern framework to work with the client part Angular, relational control systems, databases MariaDB HeidiSQL and its client, the technology to work with the client part of the site HTML and CSS and additional tools such as Bootstrap and SCSS. As a result, within practical implementation of software tool has been developed whose main goal is an analysis of matrix distant for recruitment in IT sector. The software tool covers general information and requirements for software, described in detail basic functionality of the software analysis and test cases for all kinds of users is presented in the article. © Springer Nature Switzerland AG 2020.

关键词： learning algorithms

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Code Property Graph-Based Vulnerability Dataset Generation for Source Code Detection 3rd

Code Property Graph-Based Vulnerability Dataset Generation f...

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3rd International Conference on Frontiers in Cyber Security, FCS 2020

作者： Guan, Zhibin Wang, Xiaomeng Xin, Wei Wang, Jiajie China Information Technology Security Evaluation Center Beijing China

ISBN: (纸本)9789811597381

Most existing deep learning-based source code vulnerability detection methods focus on the design of different deep learning algorithms to improve the accuracy of source code vulnerability detection, ignoring the obvious problems: firstly, lack of sufficient source code vulnerability data;secondly, lack of high-quality code data for deep learning algorithms. Therefore, we propose a code attribute graph-based data generation method for deep learning based source code vulnerability detection. The proposed method tries to represent source code based on code attribute graph, which is used to extract the control flow information and data flow information of source code sequentially. And the code data can be generated by retrieval-matching method. The advantage of this method is that it can extract rich semantic information of source code, and the generated code slices can be used for deep learning algorithms directly. Experimental results show that the proposed method can generate a large number of high-quality code data, which can provide data for deep learning-based source code vulnerability detection. © 2020, Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Issuesand Strategies for Modeling Gene Regulatory Networks

Issuesand Strategies for Modeling Gene Regulatory Networks

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2020 International Conference on Research, Innovation, Knowledge Management and Technology Application for Business Sustainability, INBUSH 2020

作者： Bansal, Bhavana Nanda, Aparajita Sahoo, Anita Jaypee Institute of Information Technology Department of Cse and It Noida India

ISBN: (纸本)9781728117690

The objective of system biology is to develop the empirical mathematical model of biological systems formed by the interactions between various complex biological data. Gene regulatory networks are developed to explain the complex nature of gene regulation using genomic data. This paper discusses the construction of gene regulatory network using multi-omics genomic data. The major focus is on exploring various modeling schemes, issues and challenges in the construction of gene regulatory network. In this paper, the outline of different techniques, their strengths and limitations have been discussed. © 2020 IEEE.

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