Simultaneous detection for multiple anomaly data in internet of energy based on random forest

Li, Qiang; Zhang, Limei; Zhang, Guanghui; Ouyang, Hanyi; Bai, Muke

doi:10.1016/j.asoc.2023.109993

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…During the prediction phase, each tree in the forest "votes" to predict the outcome for a new data point in classification tasks, while in regression tasks, the predictions of all trees are averaged to yield the result. This ensemble approach combines the individual strengths of multiple trees to yield a more correct and robust prediction than any single decision tree could provide [13].…”

Section: Work Methodology 211 Random Forest Algorithmmentioning

confidence: 99%

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Rhouas,

El Attaoui,

El Hami

2024

IJECE

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In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.

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Section: Work Methodology 211 Random Forest Algorithmmentioning

confidence: 99%

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Rhouas,

El Attaoui,

El Hami

2024

IJECE

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“…An RF [39] algorithm, designed as an ensemble learning mechanism, includes multiple decision trees. It proves to be very effective because not only does a single tree decide which class a new data point is assigned to, but a whole group of trees does it by the majority vote of all of them.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

The development of a model for the threat detection system with the use of machine learning and neural network methods

Ussatova,

Zhumabekova,

Karyukin

et al. 2024

ijirss

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This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

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“…The study first identifies and cleanses the original "dirty data," and the common data cleaning methods are statistical 3σ criterion, box plots, and clustering methods based on machine learning, local anomaly factors, isolated forests, and deep learning methods [5][6][7][8]. Due to the diversity of line loss problems, false alarms, omissions, and other problems in the detection process, the above methods present a significant human impact on anomaly data detection.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning

Jian-Yu Ren,

Jian-Wei Zhao,

Nan Pan

et al. 2023

Int. j. eng. technol. innov.

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The distribution network line loss rate is a crucial factor in improving the economic efficiency of power grids. However, the traditional prediction model has low accuracy. This study proposes a predictive method based on data preprocessing and model integration to improve accuracy. Data preprocessing employs dynamic cleaning technology with machine learning to enhance data quality. Model integration combines long short-term memory (LSTM), linear regression, and extreme gradient boosting (XGBoost) models to achieve multi-angle modeling. This study employs regression evaluation metrics to assess the difference between predicted and actual results for model evaluation. Experimental results show that this method leads to improvements over other models. For example, compared to LSTM, root mean square error (RMSE) was reduced by 44.0% and mean absolute error (MAE) by 23.8%. The method provides technical solutions for building accurate line loss monitoring systems and enhances power grid operations.

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Simultaneous detection for multiple anomaly data in internet of energy based on random forest

Cited by 7 publications

References 46 publications

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

The development of a model for the threat detection system with the use of machine learning and neural network methods

Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning

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