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检索条件"任意字段=21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020"

共 111 条记录，以下是31-40 订阅

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Trading Cryptocurrency with Deep Deterministic Policy Gradients 21st

Trading Cryptocurrency with Deep Deterministic Policy Gradie...

引用

21st international conference on intelligent data engineering and automated learning

作者： Tummon, Evan Raja, Muhammad Adil Ryan, Conor Avaya Galway Ireland Univ Limerick Dept Comp Sci & Informat Syst Limerick Ireland

ISBN: (纸本)9783030623616;9783030623623

the volatility incorporated in cryptocurrency prices makes it difficult to earn a profit through day trading. Usually, the best strategy is to buy a cryptocurrency and hold it until the price rises over a long period. this project aims to automate short term trading using Reinforcement learning (RL), predominantly using the Deep Deterministic Policy Gradient (DDPG) algorithm. the algorithm integrates with the BitMEX cryptocurrency exchange and uses Technical Indicators (TIs) to create an abundance of features. Training on these different features and using diverse environments proved to have mixed results, many of them being exceptionally interesting. the most peculiar model shows that it is possible to create a strategy that can beat a buy and hold strategy relatively effortlessly in terms of profit made.

关键词： Reinforcement learning Cryptocurrency Trading

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Machine learning Methods for Fake News Classification 20th

Machine Learning Methods for Fake News Classification

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Ksieniewicz, Pawel Choras, Michal Kozik, Rafal Wozniak, Michal Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Fac Elect Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland UTP Univ Sci & Technol Dept Teleinformat Syst Bydgoszcz Poland

ISBN: (纸本)9783030336172;9783030336165

the problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users. Such false information should be detected as soon as possible to avoid its negative influence on the readers and in some cases on their decisions, e.g., during the election. therefore, the methods which can effectively detect fake news are the focus of intense research. this work focuses on fake news detection in articles published online and on the basis of extensive research we confirmed that chosen machine learning algorithms can distinguish them from reliable information.

关键词： Fake news Online disinformation Classification Classifier ensebles Random Subspace

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A Machine learning Approach to Forecast the Safest Period for Outdoor Sports 1

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21st international conference on intelligent data engineering and automated learning

作者： Palmeira, Joao Ramos, Jose Silva, Rafael Fernandes, Bruno Anafide, Cesar Univ Minho ALGORITMI Ctr Dept Informat P-4704553 Braga Portugal

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

Vulnerable Road Users (VRUs) are all to whom danger may befall on the road, such as pedestrians and cyclists. the research here conducted aims to reduce such vulnerability by providing every VRU with a new tool to help them know when it is the safest period to do sports during the day. To achieve the proposed goal, the focus was set on Machine learning models, such as Random Forest and Multilayer Perceptrons (MLP), trained and tuned with data received from multiple sources, including the weather, pollution levels, traffic flow and traffic incidents. A mobile application was designed and conceived, making use of the model with the best performance, an MLP, to provide forecasts to VRUs. Our results show promising prospects regarding the use of Machine learning models to quantify the best period for sports during the day.

关键词： Deep learning Machine learning Multilayer perceptrons Vulnerable Road Users Road safety

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Video Semantics Quality Assessment Using Deep learning 21st

Video Semantics Quality Assessment Using Deep Learning

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21st international conference on intelligent data engineering and automated learning

作者： Jesus, Rui Silveira, Barbara Correia, Nuno Univ Nova Lisboa NOVA LINCS FCT Caparica Portugal IPL Inst Super Engn Lisboa ADEETC Lisbon Portugal

ISBN: (纸本)9783030623647;9783030623654

this work proposes a method to assess the quality of user-generated videos (UGVs) of specific social events. the method is based on matching the semantic information extracted from videos and the information obtained from text news of the same event. Deep learning techniques are used to detect objects in the video scenes. News articles are represented by a set of relevant terms automatically extracted from the news. this paper describes our method and an evaluation of it.

关键词： User generated content Video quality assessment Semantic deep learning

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Uncertain Frequent Itemsets Mining Algorithm on data Streams with Constraints 17th

Uncertain Frequent Itemsets Mining Algorithm on Data Streams...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Yu, Qun Tang, Ke-Ming Tang, Shi-Xi Lv, Xin Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China Yancheng Teachers Univ Sch Informat Engn Yancheng Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Nowadays, many emerging applications in real-life can produce amount of uncertain data streams, while people are often interested in some aspects. To mine constrained frequent itemsets on uncertain data streams, this paper presents a method. First, determining the order of items in the transactions of data streams according to the properties of constraints;then, inserting items into the tree in order;finally, mining constrained frequent itemsets from the tree. Existing algorithms are compared with the proposed method and the performances are analyzed. Results indicate that the proposed method is effective and efficient, which mines constrained frequent itemsets when users request for the mining results and need no additional memory.

关键词： Uncertain data data streams Frequent itemsets Constraint

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Adaptive Machine learning-Based Stock Prediction Using Financial Time Series Technical Indicators 20th

Adaptive Machine Learning-Based Stock Prediction Using Finan...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Taha, Ahmed K. Kholief, Mohamed H. AbdelMoez, Walid Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Alexandria Egypt

ISBN: (纸本)9783030336172;9783030336165

Stock market prediction is a hard task even with the help of advanced machine learning algorithms and computational power. Although much research has been conducted in the field, the results often are not reproducible. that is the reason why the proposed workflow is publicly available on Github [1] as a continuous effort to help improve the research in the field. this study explores in detail the importance of financial time series technical indicators. Exploring new approaches and technical indicators, targets, feature selection techniques, and machine learning algorithms. Using data from multiple assets and periods, the proposed model adapts to market patterns to predict the future and using multiple supervised learning algorithms to ensure the adoption of different markets. the lack of research focusing on feature importance and the premise that technical indicators can improve prediction accuracy directed this research. the proposed approach highest accuracy reaches 75% with an area under the curve (AUC) of 0.82, using historical data up to 2019 to ensure the applicability for today's market, with more than a hundred experiments on a diverse set of assets publicly available.

关键词： Stock price prediction Technical indicators feature importance Adaptive stock prediction Machine learning Feature selection

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An Implementation of the "Guess Who?" Game Using CLIP 22nd

An Implementation of the "Guess Who?" Game Using CLIP

引用

22nd international conference on intelligent data engineering and automated learning

作者： Marti Sarri, Arnau Rodriguez-Fernandez, Victor Valencian Int Univ Calle Pintor Sorolla 21 Valencia 46002 Spain Dimai SL Cam & Font Calda 10 Navarcles 08270 Spain Univ Policn Madrid Sch Comp Syst Engn Calle Alan Turing Madrid 28038 Spain

ISBN: (纸本)9783030916077;9783030916084

CLIP (Contrastive Language-Image Pretraining) is an efficient method for learning computer vision tasks from natural language supervision that has powered a recent breakthrough in deep learning due to its zero-shot transfer capabilities. By training from image-text pairs available on the internet, the CLIP model transfers non-trivially to most tasks without the need for any data set specific training. In this work, we use CLIP to implement the engine of the popular game "Guess who?", so that the player interacts with the game using natural language prompts and CLIP automatically decides whether an image in the game board fulfills that prompt or not. We study the performance of this approach by benchmarking on different ways of prompting the questions to CLIP, and show the limitations of its zero-shot capabilites.

关键词： CLIP Guess who Zero-shot learning Language-image models

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Stateful Optimization in Federated learning of Neural Networks 21st

Stateful Optimization in Federated Learning of Neural Networ...

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21st international conference on intelligent data engineering and automated learning

作者： Kiss, Peter Horvath, Tomas Felbab, Vukasin Eotvos Lorand Univ Fac Informat Dept Data Sci & Engn Budapest Hungary Pazmany Peter Setany 1-C H-1117 Budapest Hungary Pavol Jozef Safarik Univ Inst Comp Sci Fac Sci Jesenna 5 Kosice 04001 Slovakia

ISBN: (纸本)9783030623647;9783030623654

Federated learning is a emerging branch of machine learning research, that is examining the methods for training models over geographically separated, unbalanced and non-iid data. In FL, on non-convex problems, as in single node training, the almost exclusively used method is mini batch gradient descent. In this work we examine the effect of using stateful training method in a federated environment. According to our empirical results with these methods, at the cost of synchronizing state variables along with model parameters, a significant improvement can be achieved.

关键词： Gradient methods

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Least Squares Approach for Multivariate Split Selection in Regression Trees 1

引用

21st international conference on intelligent data engineering and automated learning

作者： Schoene, Marvin Kohlhase, Martin Bielefeld Univ Appl Sci Fac Engn & Math Ctr Appl Data Sci Gutersloh Bielefeld Germany

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

In the context of Industry 4.0, an increasing number of data-driven models is used in order to improve industrial processes. these models need to be accurate and interpretable. Regression Trees are able to fulfill these requirements, especially if their model flexibility is increased by multivariate splits that adapt to the process function. In this paper, a novel approach for multivariate split selection is presented. the direction of the split is determined by a first-order Least Squares model, that adapts to process function gradient in a local area. By using a forward selection method, the curse of dimensionality is weakened, interpretability is maintained and a generalized split is created. the approach is implemented in CART as an extension to the existing algorithm for constructing the Least Squares Regression Tree (LSRT). For evaluation, an extensive experimental analysis is performed in which LSRT leads to much smaller trees and a higher prediction accuracy than univariate CART. Furthermore, low sensitivity to noise and performance improvements for high dimensional input spaces and small data sets are achieved.

关键词： Industry 4.0 Interpretability Curse of dimensionality Oblique Regression Trees Mixed Regression Trees CART

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intelligent Fuzzy Optimized Control for Energy Extraction in Large Wind Turbines 21st

Intelligent Fuzzy Optimized Control for Energy Extraction in...

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21st international conference on intelligent data engineering and automated learning

作者： Serrano-Barreto, Carlos Santos, Matilde Univ Complutense Madrid Madrid 28040 Spain Univ Complutense Madrid Inst Tech Knowledge Madrid 28040 Spain

ISBN: (纸本)9783030623647;9783030623654

In this paper an intelligent controller is designed to obtain the maximum power of a large floating offshore wind turbine. the control of these turbines is more complex due to the strong loads they are subjected to and the uncertainty that comes from the environment, mainly wind and waves, and from its non-linear dynamics. In this case, the control goal is to maximize the output power of the wind turbine by controlling the rotor speed. An incremental PD-type fuzzy controller has been implemented;it generates the pitch angle reference. the performance of this control scheme on the NREL 5 MW floating offshore wind turbine has been compared with the internal control that is provided within the FAST software. Results are encouraging, showing that the intelligent control strategy is able to produce more energy.

关键词： intelligent control Fuzzy logic Pitch angle Floating offshore wind turbine Renewable energy

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