An Experimental Machine Learning Approach for Mid-Term Energy Demand Forecasting

Yan, Shu‐Rong; Tian, Man-Wen; Alattas, Khalid A.; Mohammadzadeh, Ardashir; Sabzalian, Mohammad Hosein; Mosavi, Amir

doi:10.1109/access.2022.3221454

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…Any artificial intelligence application is mainly dependent on data [1]. Due to its numerous uses, AI has been incorporated in many areas such as healthcare [2][3][4][5], agriculture [6,7], multi-class image classification [8], image caption prediction [9], fake image identification [10], and other purposes [11][12][13]. In the majority of real-world classification applications, the training data shows a distribution with a long tail.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

et al. 2023

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Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets.

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Section: Introductionmentioning

confidence: 99%

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

et al. 2023

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“…Inherent characteristics of the vehicles chassis and suspension could also be researched as complex road conditions, and they could also be the direction of long-range calculations and practical simulations. Another direction of scientific research could be associated with the evaluation of these parameters through the use of methodologies such as machine learning and fuzzy systems, such as artificial intelligence [42,43], which could improve such calculations.…”

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confidence: 99%

Research Determining the Priority Order of Forces Acting on a Vehicle Transporting Logs

et al. 2023

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When transporting different types of cargo, the factors affecting the cargo must be assessed to ensure a safe and fast transportation process. However, the specificity of the cargo means that the standard assessment methods do not reflect the existing conditions or are standard at the theoretical and/or recommendation level. Therefore, in the event of a traffic accident, when a vehicle or cargo is damaged or other traffic participants are put at rink, assessment of the situation is difficult and requires an expert assessment. Then, the most optimal means of accomplishing this is to simulate transportation parameters with a specific vehicle and cargo. The transportation of wooden logs is a very specific method of transportation where, in addition to the weight of the load itself, it is necessary to assess how the load affects the dynamics of the vehicle. Therefore, determining the priority of the forces that act on the vehicle carrying wooden logs becomes very important in order to model the transport parameters of this cargo. This article uses an analytical survey, an expert survey and its analysis, and methods to determine correlations. Given the fact that the main forces acting on the load most often manifest during a turn, this study assessed road turns and the respective speed on them as the main parameters. Other parameters, such as road roughness and roundabouts, were not assessed, because they would be an object of long-distance research. The study results revealed that the main forces affecting a vehicle transporting logs manifest in the following priority order: inertial forces, gravity forces, frictional forces and, finally, centrifugal forces.

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