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

作者： Song, Yizhe China Univ Polit Sci & Law Business Sch Beijing Peoples R China

ISBN: (纸本)9781665416061

Deep learning is the main focus of the machine learning field. However, it is noticeable that some researchers utilize broad learning to efficiently solve some problems in this field. Under such conditions, even though deep learning has obtained some breakthroughs, a comprehensive learning system is still valuable. Comparing the advantages and disadvantages of these two methods is difficult to achieve a general conclusion. However, outcomes about the performances of models are possible for a particular problem or data set. In this article, all the experiments are based on MNIST data set, using Multilayer Perceptron models to test the differences of broadness and depth. Nevertheless, the result of experiments shows it is tough to choose the best from these models. This may derive from the complexity of model structures.

关键词： MNIST ANN broad learning deep learning

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Plant Disease Detection Using machine learning Approaches: A Review 4th

Plant Disease Detection Using Machine Learning Approaches: A...

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4th international conference on data, engineering, and Applications, IDEA 2022

作者： Saraf, Puja Dipak Patil, Jayantrao Bhaurao Department of Computer Engineering R. C. Patel Institute of Technology Shirpur425405 India

ISBN: (纸本)9789819700363

Agriculture plays an important role in the country’s development, and it is the key source of food for the population of the world. But in developing countries like India, Pakistan and Nepal, the condition of the agriculture sector is getting worse day by day. There are many reasons behind it, but one of the reasons is the degeneration of crops by pests and disease. Agriculture solely depends on the biological health of the seeds, crops, information on weather conditions soils and biotic features liable for the crop diseases. Robust diagnostic methods are accomplished by dispensation and scrutinizing large quantities of records near get additional consistent material and ample more accurate predictions remain vital. Deep learning, image processing, soft computing and data mining are likely to play a vital role in recognizing plant diseases. This paper aims to review existing studies and investigations using the recent practices to solve a variety of agricultural problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Noise-intensification data augmented machine learning for day-ahead wind power forecast

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ENERGY REPORTS 2022年 8卷 916-922页

作者： Chen, Hao Birkelund, Yngve Batalden, Bjorn-Morten Barabadi, Abbas UiT Arctic Univ Norway Dept Technol & Safety N-9019 Tromso Norway UiT Arctic Univ Norway Dept Phys & Technol N-9019 Tromso Norway UiT Arctic Univ Norway Arctic Ctr Sustainable Energy N-9019 Tromso Norway

The day-ahead wind power forecast is essential for the designation of dispatch schedules for the grid and rational arrangement for production planning by power generation companies. This paper specifically investigates the effect of adding noise to the original wind data for forecasting models. Linear regression, artificial neural networks, and adaptive boosting predictive models based on data-intensification white noise and uniform noise are evaluated in detail and their superiority over the original data-based models is compared. The results demonstrate that solely injecting noise into the dataset can statistically boost the performance of all forecasting models with learning algorithms. The findings of this study suggest a fresh perspective for developing wind power prediction models and carry certain wind energy engineering merits. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： machine learning Noise Power forecast Statistical inference Wind energy data science

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ON THE CALIBRATION OF METOCEAN TIME SERIES USING machine learning 41

ON THE CALIBRATION OF METOCEAN TIME SERIES USING MACHINE LEA...

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ASME 41st international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Podrazka, Olga Renac, Laury Enet, Francois Aktis Hydraul Zwolle Netherlands Aktis Hydraul La Rochelle France

ISBN: (纸本)9780791885895

Nowadays, numerical model data is one of the primary inputs to all metocean studies, whether for deep-water locations or coastal applications. This paper presents the use of machine learning to calibrate long term metocean time series of wind and wave parameters obtained from numerical models against measurement records, usually covering shorter periods. We present the added value of machine learning compared to standard calibration methods to improve data used as primary input to both operability studies and engineering design studies. Time series of wind and wave parameters obtained from global numerical hindcast data sets are compared to oceanographic buoy measurements. We investigate the improvement brought by machine learning methods on the quality of the calibrated populations for the bulk of the distributions, but also the agreement between the calibrated data and the measurements for extreme events, not only for peak values but also for storm profiles. We evaluate the reliability of the method by comparing the results over different periods at 1 location and with varying length of training, validation and test sets.

关键词： Calibration Time series machine learning Wind Computer simulation Waves engineering design Reliability Water Storms Shorelines Buoys

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Comparison Model for Forecasting the Total Population in Bangkok 9

Comparison Model for Forecasting the Total Population in Ban...

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Joint 9th international conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Tipwong, Nattanicha Jitpattanakul, Anuchit Phaphan, Wikanda Nontapa, Chalermrat Mekruksavanich, Sakorn King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Chiang Mai University Faculty of Science Department of Statistics Chiang Mai50200 Thailand Research Group in Statistical Learning and Inference Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

ISBN: (纸本)9798350318241

The primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok's overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, Department of Provincial Administration. This dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. The second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. The evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis. © 2024 IEEE.

关键词： Mean square error

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Construction and Application of Credit card fraud crime prediction model based on SVM Algorithm 3

Construction and Application of Credit card fraud crime pred...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Yu, Peng Liu, Xinyan College of Investigation People's Public Security University of China Beijing China

ISBN: (纸本)9781510664814

In recent years, with the rapid development of the Internet, telecommunications and financial industry, credit card fraud as a non-traditional crime cases surge. People's dependence on credit card payments and mobile payments has been further deepened, which also provides a fertile soil for credit card fraud. This paper uses SVM Algorithm to build a credit card fraud prediction model, and verifies the prediction performance through data experiments, and finds that it has a high accuracy for identifying credit card fraud. If the model is applied to real life, it can avoid the loss caused by credit card fraud in time, which has a broad development prospect. © 2023 SPIE.

关键词： machine learning

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Research on anomaly detection of wireless data acquisition in power system based on spark

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ENERGY REPORTS 2022年 8卷 1392-1404页

作者： Pei, Caiyan Zhang, Shuai Zeng, Xiaoning Hebei Normal Univ Sci & Technol Sch Math & Informat Sci & Technol Qinhuangdao 066004 Hebei Peoples R China

In the era of big data, the network data of power system is more and more complex. Due to the limitation of data storage and processing capacity, the abnormal data detection of power grid terminal information system has the problems of low accuracy and high false alarm rate. The original machine learning algorithm with good detection effect is limited by the processing capacity and storage space of the traditional platform, and the detection effect and efficiency are significantly reduced. This paper takes improving the detection accuracy of abnormal data as the main research target, and designs an abnormal data behavior analysis program based on the Internet of Things under the Spark framework combined with improved Support Vector machine (SVM) and random forest algorithm. The parallel SA_SVM_RF anomaly data behavior detection model based on Spark is mainly studied and applied to real-time detection. Combined with the respective advantages of Internet of Things technology and machine learning in anomaly data detection, the detection capability and rate of power grid anomaly data detection model are further improved. Experimental tests show that the proposed program is superior to traditional methods in data anomaly detection efficiency and quality, and has certain research significance in the field of power grid security. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Internet of Things Wireless data acquisition data anomaly Spatial vector model

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From data to Impact: Lessons Learned in Leveraging Digitalization and machine learning to Boost Operational Efficiency and ESG Performance in Drilling Rigs

From Data to Impact: Lessons Learned in Leveraging Digitaliz...

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2025 SPE/IADC international Drilling conference and Exhibition, DC 2025

作者： Poludasu, S. Hidad, S. Groh, A. Godumagadda, A. van Wissenkerke, M. Snijder Harrist, J. Patterson UTI Drilling Company LLC HoustonTX United States

ISBN: (纸本)9781959025641

While there is significant enthusiasm for leveraging data to drive optimization, discussions rarely focus on the challenges associated with the real-time deployment of data-driven workflows. This paper addresses these challenges, highlighting the crucial need for synergy between data, engineering expertise, technology, and organizational commitment to convert data-driven insights into tangible efficiency gains and reduced emissions. It details lessons learned from a drilling contractor's perspective in developing and implementing a data-driven performance evaluation system for a fleet of over a hundred onshore rigs. This paper highlights the significance of robust data architecture, which is crucial for improving the system's reliability in developing representative models and metrics for the rig performance evaluation system. These metrics cover multiple dimensions of rig performance, including Health, Safety, and Environmental (HSE) standards, drilling performance, resource management, and financial metrics. The selection of these metrics and their targets was informed by data trends and stakeholder expectations, with a strong emphasis on prioritizing safety, ensuring that all targets adhered to strict safe operating standards. Initially introduced in 2019, the performance evaluation system underwent a significant update in early 2022 to better reflect the benefits of advancements in automation, technology, and training available to rig crews. An ESG metric was added to the evaluation system to support the organization's commitment to sustainability. This ESG metric employs ensemble machine learning and on-rig edge devices to assess CO2 emissions against modeled targets. The automated system detailed in this paper produces standardized scores based on these metrics, enabling the monthly assessment of individual rig performance, and ensuring continuous improvement aligned with environmental and efficiency goals. Since updating the evaluation system in early 2022, s

关键词： Efficiency

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Third international conference on machine learning and Computer Application, ICMLCA 2022

Third International Conference on Machine Learning and Compu...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

ISBN: (纸本)9781510664814

The proceedings contain 197 papers. The topics discussed include: design and practice of university education big data application support system;research on high-quality patent evaluation methods based on machine learning;research on key technologies of atmospheric carbon emission monitoring and early warning based on big data and spectral measurement;a transfer learning framework based on adaptive spatial spectral relational networks for hyperspectral image classification;research on abnormal network traffic detection method based on machine learning;research on anti-noise performance of data driven double frequency-hopping OFDM information transmission method;adaptive federated learning aggregation strategies based on mobile edge computing;a review of remote sensing image road extraction research based on deep learning;research on image classification mechanism based on self-supervised learning;and pool fire rendering method based on improved particle system with collision detection algorithm.

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Literature Review of Personalizing learning Recommendation Systems Using machine learning in Chinese Higher Education 24

Literature Review of Personalizing Learning Recommendation S...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Huang, Mingjing Cheong, Ngai Liu, Jiaqi Zhang, Zhuofan Faculty of Applied Sciences Macao Polytechnic University China Intelligent Transportation Engineering Institute Guangdong Communication Polytechnic Guangdong Guangzhou China

ISBN: (纸本)9798400717468

With the rapid development of information technology, personalized education recommendation systems have gained widespread attention and rapid development in China’s education sector. These systems provide customized learning resources and course recommendations to meet the individual needs of different students by analyzing their learning behaviors, preferences, and performance. This study comprehensively searched 45, 623 literatures from 2002 to 2022, focusing on the current status, key technologies, and application practices of educational recommendation systems in China. It first investigates the basic deep learning recommendation methods used in current personalized learning recommendation systems, and then summarizes the current status of machine learning techniques applied in personalized learning recommendation systems for higher education in China. Finally, through a comprehensive analysis of the existing literature, we discuss the current challenges and future development direction of China’s educational recommendation systems, and put forward improvement suggestions for domestic educational recommendation systems, aiming to fill the research gaps and promote technological progress and innovation. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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