A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Byun, Yung-Cheol; Jeong, Ok‐Ran

doi:10.1038/s41598-023-35476-y

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…However, the stack-based ensemble technique introduces an additional layer of complexity to the model, potentially making its decision-making process less transparent and comprehensible [13]. Researchers are actively exploring methods to enhance the explainability of stacking models and to make them transparent for real-world applications [51][52][53][54]. In our study, the feature permutation technique was used for the RF model [31].…”

Section: Discussionmentioning

confidence: 99%

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

Kuo,

Chen,

et al. 2024

Eur Radiol Exp

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Background This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. Relevance statement The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. Key points • Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

Kuo,

Chen,

et al. 2024

Eur Radiol Exp

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Section: A Test Objects Main Goals and Data Collectionmentioning

confidence: 99%

“…Several studies on tools made of different materials, but subjected to the same operating conditions, were grouped into a singular category, showing significant findings [48][49][50][51]. In [48], five types of solar panels were used to collect data on different types of contamination. Similarly, a stacked ensemble learning technique was employed to explore traffic sign recognition [49], malware detection [50], and sand-dust storm forecasting [50].…”

Section: A Test Objects Main Goals and Data Collectionmentioning

confidence: 99%

Stacked Ensemble Deep Learning for Outdoor Insulator Surface Condition Classification: A Profound Study on Water Droplets

Serikbay,

Bagheri,

Zollanvari

2023

IEEE Access

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Insulators are vital protection and isolation barriers used in power transmission systems. To prevent unexpected failures caused by severe weather conditions, it is important to develop intelligent insulator fault prognosis systems. To this end, this study has focused to examine and analyze normal (clean) and contaminated (wet) insulators through a new technique that can act as a catalyst for possible future solutions to the data-driven classification of insulator surface conditions. In particular, a novel stacked ensemble learning based on six pretrained deep convolutional neural networks (CNNs), which are used as level-0 generalizers in the stacked structure, is proposed. As the level-1 generalizer that is used in the stacked structure, a majority vote is considered among selected subsets of level-0 generalizers. In other words, training the level-1 generalizer is equivalent to selecting the best subset of pretrained CNNs for classifying a clean insulator surface from those sprayed with water droplets. Since a wetted insulator surface can enhance the electric field intensity and may lead to flashover when combined with other contaminants (dust, soil, cement, etc.), water droplets are considered a type of contamination in our study. Considering a tradeoff between performance and model complexity in training the level-1 generalizer points to two combinations of pretrained CNNs, namely, EfficientNetB2-ResNet50-Xception, and MobileNet-DenseNet121-Xception. Our empirical results show that (i) both combinations lead to better performance when compared with individual pretrained models, and (ii) the latter combination leads to a considerably lower complexity (~39% less parameters) at the expense of ~9% reduction in accuracy.INDEX TERMS deep learning, high-voltage insulators, pretrained CNN architectures, stacked ensemble learning.

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An interpretable Bayesian deep learning-based approach for sustainable clean energy

Ezzat,

Ahmed,

Soliman

et al. 2024

Neural Comput & Applic

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Sustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet’s efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods.

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A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Cited by 3 publications

References 32 publications

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

Stacked Ensemble Deep Learning for Outdoor Insulator Surface Condition Classification: A Profound Study on Water Droplets

An interpretable Bayesian deep learning-based approach for sustainable clean energy

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