Electricity theft poses a significant challenge to power distribution companies, leading to substantial economic losses and threatening the stability and reliability of power systems worldwide. This study addresses th...
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Electricity theft poses a significant challenge to power distribution companies, leading to substantial economic losses and threatening the stability and reliability of power systems worldwide. This study addresses the complexities of detecting electricity theft in regions that rely on traditional meters, which record low-sampling-rate data. Due to the asynchronous nature and low frequency of data collection, existing machine learning models often struggle with noise and class imbalance, resulting in reduced detection accuracy. To overcome these challenges, a novel framework developed in this article that combines advanced preprocessing techniques with machine learning. The developed framework includes data conversion to monthly averages, normalization, and the application of the Synthetic Minority Over-sampling Technique to balance the dataset. An Extreme Gradient Boosting algorithm is then applied for effective electricity theft detection. The study utilizes a real dataset from a northern Iranian province, comprising 119,131 customers with meter readings taken at 40 to 60 days intervals. The proposed approach offers a promising solution for electricity theft detection in regions employing traditional metering systems, with the accuracy and precision of 99.7 % and 98.2 %, respectively.
In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal *** this purpose,an initial dataset,containing the hardness values of 270 compounds and count...
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In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal *** this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning *** on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(xgb)*** validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and Ta),Al_(2)O_(3),and FeB_(4),showed that the xgb model was able to reproduce load-dependent hardness behaviors of these *** addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.
[...]there has been no related research report on machine learning and deep learning prediction models for chronic pain after breast cancer surgery.[...]we constructed a prediction model of chronic pain after breast c...
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[...]there has been no related research report on machine learning and deep learning prediction models for chronic pain after breast cancer surgery.
[...]we constructed a prediction model of chronic pain after breast cancer surgery using a variety of machine learning and deep learning techniques.
[...]we did not assess factors such as psychological or genetic characteristics, and they may be potential predictors of chronic pain after breast cancer surgery.
[...]the results of this study show that a variety of machine learning algorithms and deep learning algorithm models can predict the occurrence of chronic postoperative pain in breast cancer patients.
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