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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33664 条记录，以下是4971-4980 订阅

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Research on Intelligent Information System Construction of Online Class Based on Computer Big data and Virtual Reality Technology

Research on Intelligent Information System Construction of O...

引用

IEEE international conference on Electrical engineering, Big data and Algorithms (EEBDA)

作者： Zhou, Xiuying Hu, Yong Anhui Sanlian Univ Sch Foreign Studies Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781665416061

In view of the current situation of decontextualized or weak-contextualized teaching mode in college English class, the paper proposes to employ big data technology to construct an ecological teaching context in online English class to enhance language immersion. Guided by the theory of situational cognitive learning, the situational elements of ecological teaching in college English class are analyzed and designed, and the big data virtual situation creation system is realized.

关键词： computer big data ecological teaching system English class online courses construction

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Federated learning for Air Quality Index Prediction: An Overview 14

Federated Learning for Air Quality Index Prediction: An Over...

引用

14th international conference on Knowledge and Systems engineering, KSE 2022

作者： Le, Duy-Dong Tran, Anh-Khoa Dao, Minh-Son Nazmudeen, Mohamed Saleem Haja Mai, Viet-Tiep Su, Nhat-Ha Ueh Vinh Long Campus University of Economic Ho Chi Minh Ho Chi Minh Viet Nam Big Data Integration Research Centers Nict Tokyo Japan Utb School of Business Universiti Teknologi Brunei Bandar Seri Begawan Brunei Academy of Cryptography Techniques Ho Chi Minh Viet Nam Fpt University Ho Chi Minh Viet Nam

ISBN: (数字)9781665452816

ISBN: (纸本)9781665452809

The air quality index forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. We have examined over 70 carefully selected papers in this scope and discovered that multi-model federated learning is the most effective technique that could enhance the air quality index prediction result. Therefore, this mechanism needs to be considered by science community in the coming years. © 2022 IEEE.

关键词： Forecasting

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Foreign Currency Exchange Market Prediction using machine learning Techniques

Foreign Currency Exchange Market Prediction using Machine Le...

引用

2022 IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Tak, Adriel Li Chi Logeswaran, Rajasvaran Asia Pacific University of Technology and Innovation Technology Park Malaysia

ISBN: (纸本)9781665483162

The importance of predicting foreign currency exchange rate is evident in both academic and business sectors, despite financial time series data being known to be chaotic, noisy and dynamic. Even though this problem has been vastly explored and studied since 1971, to the best of our knowledge, there is yet to be an effective systematic or theoretical framework for approaching the problem. Prior traditional approaches in predicting the future currency rate relied on technical and fundamental analysis, but over recent decades, prolific machine learning models capable of recognizing complex patterns, have been dominating the field with promising accuracy. Furthermore, researchers have demonstrated that hybridizing appropriate machine algorithms can improve the predictive ability, although their sustainability is still questionable. Of late, behavioral economist supposed that finance market is driven by sentiment of the market participants, thus, text mining has further enhanced the potential of machine learning techniques, whereby unstructured data for example news, forums and blogs can be used as features. These evaluations terminate by the place of insight recommendations for outlook research. © 2022 IEEE.

关键词： Sentiment analysis

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On Valuing the Impact of machine learning Faults to Cyber-Physical Production Systems

On Valuing the Impact of Machine Learning Faults to Cyber-Ph...

引用

IEEE international conference on Omni-Layer Intelligent Systems (IEEE COINS)

作者： Cody, Tyler Adams, Stephen Beling, Peter Freeman, Laura Virginia Tech Natl Secur Inst Arlington VA 22203 USA

ISBN: (数字)9781665483568

ISBN: (纸本)9781665483568

machine learning (ML) has been applied in prognostics and health management (PHM) to monitor and predict the health of industrial *** use of PHM in production systems creates a cyber-physical, omni-layer system. While ML offers statistical improvements over previous methods, and brings statistical models to bear on new systems and PHM tasks, it is susceptible to performance degradation when the behavior of the systems that ML is receiving its inputs from changes. Natural changes such as physical wear and engineered changes such as maintenance and rebuild procedures are catalysts for performance degradation, and are both inherent to production systems. Drawing from data on the impact of maintenance procedures on ML performance in hydraulic actuators, this paper presents a simulation study that investigates how long it takes for ML performance degradation to create a difference in the throughput of serial production system. In particular, this investigation considers the performance of an ML model learned on data collected before a rebuild procedure is conducted on a hydraulic actuator and an ML model transfer learned on data collected after the rebuild procedure. Transfer learning is able to mitigate performance degradation, but there is still a significant impact on throughput. The conclusion is drawn that ML faults can have drastic, non-linear effects on the throughput of production systems.

关键词： machine learning prognostics and health management fault diagnosis production systems maintenance

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Autoencoder-Based Attribute Noise Handling Method for Medical data 1

引用

29th international conference on Neural Information Processing

作者： Ranvier, Thomas Elgazel, Haytham Coquery, Emmanuel Benabdeslem, Khalid Univ Lyon UCBL CNRS INSA LyonLIRISUMR 5205 43 Bd 11 Novembre 1918 F-69622 Villeurbanne France

ISBN: (数字)9789819916450

ISBN: (纸本)9789819916443;9789819916450

Medical datasets are particularly subject to attribute noise, that is, missing and erroneous values. Attribute noise is known to be largely detrimental to learning performances. To maximize future learning performances, it is primordial to deal with attribute noise before performing any inference. We propose a simple autoencoder-based pre-processing method that can correct mixed-type tabular data corrupted by attribute noise. No other method currently exists to entirely handle attribute noise in tabular data. We experimentally demonstrate that our method outperforms both state-of-the-art imputation methods and noise correction methods on several real-world medical datasets.

关键词： data Denoising data Imputation Attribute Noise machine learning Deep learning

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A Demand Forecasting Method for High Value-Added Agri - Food Based on machine learning and Time Series Analysis

A Demand Forecasting Method for High Value-Added Agri - Food...

引用

2023 international conference on Computer Science and Automation Technology, CSAT 2023

作者： Zhao, Jianyi Li, Mi Zhang, Jingya Qi, Fangzhong Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9798350371437

This paper presents a hybrid method that combines machine learning and time series forecasting model techniques to enhance the precision and reliability of forecasts. After establishing a learning model for high value-added agri-food demand forecasting, we selected the annual demand for a certain high value-added agri-food as the target value. Other economic indicators, namely the National Income Index, population, and residents' consumption level, were chosen as the key influencing factors affecting product demand. We collected actual demand data sequences from Zhejiang Province for the years 2006-2022 as training and test samples. Comparing the forecasting results, it was found that this hybrid algorithm is an appropriate method for forecasting. Compared to other individual models, it demonstrates higher accuracy and lower mean absolute errors in predicting the demand for high value-added agri-food. © 2023 IEEE.

关键词： machine learning

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A Univariate and multivariate machine learning approach for prediction of significant wave height

A Univariate and multivariate machine learning approach for ...

引用

OCEANS Hampton Roads conference

作者： Domala, Vamshikrishna Kim, Tae-Wan Seoul Natl Univ Dept Naval Architecture & Ocean Engn Seoul South Korea

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

Predicting significant wave height is important for many engineering projects on the coastline and it is also important for present and future shipping industry. In the present study machine learning and deep learning approaches are employed to predict the significant wave height. The exploratory data analysis is performed on collected meteorological wave data to study the data type and data is filtered and fed in to the machine learning algorithms. Before the analysis the data is divided in to two parts, five years data for training and one-year data for testing. A univariate and multivariate analysis is performed with support vector machine, regression models and deep learning models Long Short-Term Memory (LSTM) and CNN. The common inputs to the models are wind speed, wind direction, gust speed, pressure, sea surface temperature to predict the significant wave height and these inputs are based on correlations with target feature. All the models are trained on 5 years data and tested for the prediction of one-year data. The performance of each model is studied with different error metrics and based on the error metrics the models are compared. The proposed models show an excellent method for predicting the significant wave height.

关键词： Significant wave height machine learning deep learning univariate multivariate

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GENERATIVE ADVERSARIAL DESIGN ANALYSIS OF NON-CONVEXITY IN TOPOLOGY OPTIMIZATION

GENERATIVE ADVERSARIAL DESIGN ANALYSIS OF NON-CONVEXITY IN T...

引用

ASME international Design engineering Technical conferences / 42nd Annual Computers and Information in engineering conference (IDETC-CIE) / 48th Design Automation conference (DAC)

作者： Hertlein, Nathan Gillman, Andrew Buskohl, Philip R. Air Force Res Lab Dayton OH 45433 USA Air Force Res Lab Mat & Mfg Directorate Dayton OH 45433 USA

ISBN: (纸本)9780791886236

Material penalization and filtering schemes are key strategies applied to topology optimization (TO) to promote more discrete and manufacturable designs. However, these modifications introduce fluctuations in the design landscape that amplify non-convexity and influence the local minima identified by TO. Harnessing the machine learning approach of generative adversarial networks (GAN), we investigate the role of penalization and filtering by comparing the designs between TO and GAN-based TO surrogates. A total of 17 GANs were constructed to predict 2D minimum compliance topologies across a set of penalization factors and filters, each interpolating a design space of 270,000 boundary condition and loading scenarios. The prevalence of GAN-predicted topologies with better compliance than TO-calculated topologies was estimated via a random sampling of the design space. GAN 'over-performance' occurs across material penalization and filtering conditions, where the frequency tends to increase as penalization increases. Analysis of this test set is leveraged to highlight trends regarding the conditions under which this 'over-performance' occurs, and the geometric characteristics these designs exhibit. Collectively, this study presents an alternative method to characterize the effects of penalization and filtering on design outcomes and motivates the use of data-driven surrogates to augment traditional approaches.

关键词： Design Optimization Topology Filtration Fluctuations (Physics) machine learning Performance Boundary-value problems Filters

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Deep learning based Detection, Segmentation and Vision based Pose Estimation of Staircase 1

Deep Learning based Detection, Segmentation and Vision based...

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1st international conference on the Paradigm Shifts in Communication, Embedded Systems, machine learning and Signal Processing, PCEMS 2022

作者： Rekhawar, Nilakshi Govindani, Yogesh Rao, Neeraj Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9781665459044

Mobile robots can move around in the surrounding. They are used in military, industrial applications, for surveillance tasks, etc. These tasks involve multi-floor navigation through the staircase. For efficient and safe stair climbing, it is necessary for the robot to align itself with the staircase. This paper presents a deep learning based approach for staircase detection, semantic segmentation of stair edges and vision based techniques with statistical operations for pose estimation of the staircase. The objective is to solve the problem of staircase traversing for mobile robots without any prior knowledge of the stair geometry and location by iteratively calculating the pose of the staircase from the robot's point of view till it gets aligned with the staircase. The experimental results of staircase detection, semantic segmentation and pose estimation algorithms are presented further in the paper. © 2022 IEEE.

关键词： Stairs

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Image processing variability in pore structural investigations: conventional thresholding vs. machine learning approaches 6

Image processing variability in pore structural investigatio...

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6th international conference on Fault and Top Seals 2022, FTS 2022

作者： Shi, X. Misch, D. Vranjes-Wessely, S. Montanuniversitaet Leoben Australia

ISBN: (纸本)9789462824249

This work compares the conventional thresholding and advanced machine learning methods in image processing of broad ion beam - scanning electron microscopy (BIB-SEM) mapping data. The test data set consists of representative BIB-SEM maps acquired from 12 Middle Miocene mudstone samples within a single stratigraphic interval in the Vienna Basin, Austria. Pore space segmentation was processed using the deterministic thresholding method in the software ImageJ and the machine learning-based pixel classification in the open-source toolkit ilastik. Total porosity, as well as pore size distributions and pore morphology resolved by ImageJ and ilastik were compared to determine the impact of different image processing approaches on the resulting segmentations. Porosity and pore size distributions obtained by ImageJ and ilastik are comparable;however, variations in pore boundary delineation and feature identification inevitably influence the results. © 2022 international conference on Fault and Top Seals

关键词： Pore size

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