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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是331-340 订阅

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Predicting Energy Market Imbalance Prices with Random Forest: A Rolling Window Approach 3

Predicting Energy Market Imbalance Prices with Random Forest...

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3rd international conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Makrides, Andreas C. Loizidis, Stylianos Charalambous, Chrysanthos Makrides, George Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (纸本)9798350375923

Balancing supply and demand is essential for electricity grid stability, particularly with the increasing integration of renewable energy sources (RES). Accurate forecasting of imbalance prices can significantly enhance grid management and market efficiency. This study focuses on predicting imbalance prices in the Finnish market using a rolling window approach with a Random Forest (RF) model, addressing the variable nature of renewable energy sources (RES). The RF model demonstrated high forecasting accuracy across three cross-validation periods, achieving a highest coefficient of determination (R²) of 0.906 and a normalized root mean squared error (nRMSE) of 2.874% in the first period. When applied to the test set, the model yielded an R² of 0.90 and an nRMSE of 4.29%, effectively capturing non-linear and complex behaviors in imbalance prices. These results indicate that the RF model is a robust tool for imbalance price prediction, aiding market participants and system operators in enhancing decision-making and planning. Future research could explore the model's application in other markets and under various price conditions. © 2024 IEEE.

关键词： balancing market energy market imbalance prices machine learning predicition rolling window

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Technology Analysis of Network Anomalous Behavior Detection Based on machine learning 3

Technology Analysis of Network Anomalous Behavior Detection ...

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3rd international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Wu, Shuguang Wang, Hongyan Wang, Yu Yan, Nanjiang Space Engineering University School of Space Information Beijing China Beijing Space Information Relay Research Centre Beijing China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

In the increasingly changing network environment, network anomalous behaviors show a trend of concealment and complexity, and it is crucial to detect anomalous behaviors and eliminate hidden dangers to enhance network system security. Researchers have applied machine learning methods to anomaly detection and achieved good results. This paper presents a review of these studies. First, the concept of anomalous behavior, its sources and its analysis methods are introduced, and then an overview of the application of machine learning methods is given, detailing in order the commonly used datasets, data pre-processing methods and the application of machine learning in anomaly detection. Finally, the advantages and disadvantages of different methods are summarized, and the main challenges and solution ideas faced in the current anomaly detection tasks are analyzed. © 2022 IEEE.

关键词： Anomaly detection

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Analysis of the Mechanism of Generative Adversarial Networks learning Based on Gray Image 3

Analysis of the Mechanism of Generative Adversarial Networks...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Geng, Chaozhi 21st Century International School Beijing100080 China

ISBN: (纸本)9781510661301

machine learning has a crucial role in people's lives. machine learning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help people with image recognition, stock prediction, etc. In addition, machine learning has a subset called deep learning (DL). DL in machine learning can help people optimize and learn models better. DL in machine learning can help people optimize and learn models better. One of them is generative adversarial networks (GANs). The other one is Convolutional Neural Networks (CNNs). First, GANs have achieved impressive results in image generation, data enhancement, etc. However, effective intervention and control of the results of generative adversarial networks is a challenging problem. Based on grayscale image data, this paper investigates the change patterns of the generation results of generative adversarial networks in the face of different input data. There are three groups in the experiment. The first group is to explore the effect of the size of the dataset on the generated images. The second group is to explore the effect of the diversity of the book dataset on the results. The third group was to explore the effect of the overall dataset on the generative images. The experimentally generated images revealed that data size and diversity are important factors that affect the quality of the results generated by generative adversarial networks. Among other things, the increase in the amount of data does not always positively affect the generative results. © 2023 SPIE.

关键词： Generative adversarial networks

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Comparative Study of Static Analysis and machine learning Approaches for Detecting Android Banking Malware 3

Comparative Study of Static Analysis and Machine Learning Ap...

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3rd international conference on Emerging Smart Technologies and applications, eSmarTA 2023

作者： Ahmed, Alaa M. Saeed, Mogeeb A. Hamood, Amr A. Alazab, Abduljabar A. Ahmed, Kamel A. Computer Networks & Distributed Systems Al-saeed It Taiz University Faculty of Engineering Taiz Yemen Taiz University Al-saeed Faculty of Engineering & It Computer Networks & Distributed Systems Department Taiz Yemen

ISBN: (纸本)9798350305333

Android currently dominates the smartphone market, accounting for an impressive market share of over 70%. However, because of its widespread acceptance, mobile operating systems have become a prime target for bad actors looking to profit from them. Particularly Android has been subjected to an increasing barrage of malware assaults, including the infamous Android Banking Trojans. This study investigates the effectiveness of static analysis in locating Android banking malware in order to counter this threat. It does so by utilizing a wide range of features, including permissions, application programming interface (API) calls, opcodes, API packages, system commands, intents, strings, services, receivers, and activities. The study suggests using machine learning techniques to assess the detection of Android malware by utilizing various sets of classifiers in order to achieve this goal. The study also uses a feature selection approach to determine which features are most useful for telling malicious code apart from good code. 500 samples of malicious code and 500 samples of benign code make up the dataset that was used. The XGboost algorithm outperforms others in terms of accuracy, achieving an impressive accuracy value of 99.5% in malware detection after conducting a thorough comparison of various classifier sets. These results demonstrate the potential of static analysis and machine learning as useful tools in fending off the growing threats posed by Android malware. © 2023 IEEE.

关键词： Android malware

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Improvised Extreme learning machine for Crop Yield Prediction 3

Improvised Extreme Learning Machine for Crop Yield Predictio...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh Chandra Amity University Computer Science and Engineering UP India National Institute of Technology Computer Science and Engineering Agartala India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

machine learning is effectively involved in crop yield prediction as a decision support tool including which crops to grow in a specific region and how to increase their yield in the reaping and growing season of the crops. A number of deep learning techniques, neural network architectures and prediction models have been employed to assist crop yield prediction research. This study proposes extreme learning machine to predict crop yield, we performed data pre-processing using Kalman Filter Algorithm, certain features have been extracted using Linear Discriminant Analysis and crop prediction is done by using an improved version of Extreme Linear machine. Yield of rice crop has been predicted based upon geography, season and area of cultivation. © 2022 IEEE.

关键词： Neural networks

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Risk-Aware Linear Bandits with application in Smart Order Routing 22

Risk-Aware Linear Bandits with Application in Smart Order Ro...

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3rd ACM international conference on AI in Finance (ICAIF)

作者： Ji, Jingwei Xu, Renyuan Zhu, Ruihao Univ Southern Calif ISE Dept Los Angeles CA 90007 USA Cornell Univ SC Johnson Coll Business Ithaca NY USA

ISBN: (纸本)9781450393768

Motivated by practical considerations in machine learning for financial decision-making, such as risk-aversion and large action space, we initiate the study of risk-aware linear bandits. Specifically, we consider regret minimization under the mean-variance measure when facing a set of actions whose reward can be expressed as linear functions of (initially) unknown parameters. We first propose the Risk-Aware Explore-then-Commit (RISE) algorithm driven by the variance-minimizing G-optimal design. Then, we rigorously analyze its regret upper bound to show that, by leveraging the linear structure, the algorithm can dramatically reduce the regret when compared to existing methods. Finally, we demonstrate the performance of the RISE algorithm by conducting extensive numerical experiments in a synthetic smart order routing setup. Our results show that the RISE algorithm can outperform the competing methods, especially when the decision-making scenario becomes more complex.

关键词： online learning bandit regret analysis machine learning theory smart order routing algorithmic trading mean-variance risk-aware decision-making

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Experimental Results Regarding Multiple machine learning via Quaternions 3

Experimental Results Regarding Multiple Machine Learning via...

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3rd international conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2023

作者： Zhu, Tianlei Zhu, Renzhe Department of Mathematics Wenzhou-Kean University Zhejiang Wenzhou325060 China

ISBN: (纸本)9781643684581

This paper presents an experimental study on the application of quaternions in several machine learning algorithms. Quaternion is a mathematical representation of rotation in three-dimensional space, which can be used to represent complex data transformations. In this study, we explore the use of quaternions to represent and classify rotation data, using randomly generated quaternion data and corresponding labels, converting quaternions to rotation matrices, and using them as input features. Based on quaternions and multiple machine learning algorithms, it has shown higher accuracy and significantly improved performance in prediction tasks. Overall, this study provides an empirical basis for exploiting quaternions for machine learning tasks. © 2023 The Authors.

关键词： Rotation

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3rd international Scientific and Practical conference on Digital and Information Technologies in Economics and Management, DITEM 2023

3rd International Scientific and Practical Conference on Dig...

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3rd international Scientific and Practical conference on Digital and Information Technologies in Economics and Management, DITEM 2023

ISBN: (纸本)9783031553486

The proceedings contain 20 papers. The special focus in this conference is on Digital and Information Technologies in Economics and Management. The topics include: Intra System Links Dynamics as a Cause of Development Cyclicity: The Simplest Equations;improving the Sustainability and Safety of the City Transport System Through the application of computer Modeling;development of Software for the Analysis of Socio-Economic Indicators Based on Neural Networks;estimation of the Cost of Developing a Digital Twin Using Virtual Reality Technologies in the Metallurgical Industry;formation of Artificial Collective Intelligence Based on the Theory of Active Systems;nonlinear Modeling of the Population State of Gregarious Locusts;designing the Architecture of a Multi-agent City Management System Using Advanced Object-Oriented Modeling;analysis of the Impact of Fintech on the Economy: Japan and Russia Case;simulation of a Tubular Pyrolysis Reactor Using Comsol Multiphysics Software;an Overview on Data Augmentation for machine learning;keyboard Handwriting as a Factor of User Authentication;development of an Algorithm for Constructing Complex Fractal Images Using the L-Systems Method for Textile and Building Materials Production Enterprises;predicting the Popularity of Social Network Publications Based on Content Analysis Using the Transformer Language Model;Classification of Building Components Using Artificial Intelligence Based on Meta-Information in BIM Models;computer Vision System for Monitoring User Attention in Interactive User Interfaces;processing of Data on Power Consumption in the Management of Energy Resources of the Regional Electrical Complex;development of a Probabilistic Model for Predicting Advertising Conversion Using Recurrent Neural Networks.

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Prediction on basketball team-rankings in CBA using past stats with weighted linear regression 3

Prediction on basketball team-rankings in CBA using past sta...

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3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Zhang, Xiwen Lee, Juiwei Yu, Yuanzheng Lu, Chenglin Nanjing Foreign Language School Nanjing210008 China Shenzhen College of International Edcuation Shenzhen518017 China Hamden Hall Country Day School HamdenCT06517 United States Wuhan Britain-China School Wuhan430022 China

ISBN: (纸本)9781510680449

This study examines the application of weighted linear regression in predicting the team standings in Chinese Basketball Association (CBA) at the end of a regular season. Using historical game stats from 2017-2022 as training set, a linear regression model has been developed to predict the outcomes of CBA regular season based on the average performance of each team, which includes 20 features in the box score. The result of the analysis indicates that the weighted linear regression model is capable of make predictions of the team rankings with an accuracy of 61.4%. While key features like points/rebounds/assists per game got considered with different positive weights, unfavorable features like turnovers and personal fouls are considered to have negative weights. The findings of this research have practical implications for CBA and other sports leagues that aren't as well studied as the NBA. The predictions of league rankings indicate the winning probability and strength of the teams, which could be referenced by fans, coaches, team officials, and even gamblers. Furthermore, the methodology discussed in this paper can be extended to other statistically recorded sports and other sports with stats records and other domains where predictive modeling is possible. Overall, the effectiveness of weighted linear regression model in predicting the outcomes of games in unpopular basketball leagues like CBA has been proved valid through this research;and the unlimited potential of machine learning in the real world has been underlined. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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Benchmarking AutoML-Supported Lead Time Prediction 3

Benchmarking AutoML-Supported Lead Time Prediction

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Bender, Janek Trat, Martin Ovtcharova, Jivka FZI Res Ctr Informat Technol Haid & Neu Str 10-14 D-76135 Karlsruhe Germany

In manufacturing companies, especially in small and medium enterprises (SME) in the make-to-order domain, predicting accurate lead times for highly customisable products poses a challenge. One avenue to tackle this challenge is using machine learning to create data-driven models. With the advent of Automated machine learning (AutoML), there is a potential also for non-data-scientists in the SMEs to utilise machine learning in a self-sufficient manner In this paper, we benchmarked three AutoML solutions on data from two SMEs and compared their models with manual predictions currently in productive use as well as simple mean models. We furthermore investigated how these solutions support the model development process. While we found that the models generated by the AutoML solutions outperformed manual predictions, labour-intensive parts of the model development process such as data understanding and pre-processing as well as feature engineering were not supported sufficiently by the benchmarked solutions. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： AutoML production data-driven analytics machine learning lead time prediction

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